EXTENDED USAGE OF ADMINISTRATIVE DATA FOR ......An analysis of cyclical trend movements, however,...

1

Federal Statistical Office (Destatis) E202

EXTENDED USAGE OF ADMINISTRATIVE DATA

FOR GERMAN STATISTICS

Final methodological report Evaluate new process data from toll collection

in flash estimating the German Industrial Production Index

Grant Agreement Number 822695 - 2018-DE-ESS-VIP-ADMIN

Authors Dr Stefan Linz Dr Claudia Fries Julia Voumllker

2

Executive summary

The project has carried out a feasibility study using statistical methods to decide whether the

Truck-Toll-Mileage Index could be used to continuously calculate a nowcast of the Industrial

Production Index in a ldquomechanisticrdquo way without requiring a resource intensive monthly expert

judgment assessing all kind of information available on the economic development

The Industrial Production Index and the Truck-Toll-Mileage Index were first presented as part of

the project Much of the goods traffic on roads can be assigned to the federal motorways

meaning that the Truck-Toll-Mileage Index provides a good indicator of total road freight

transport The index is calculated at the German Federal Office for Goods Transport and provided

monthly to the German Federal Statistical Office for seasonal adjustment and final publication as

standalone business cycle indicator The seasonal adjustment and publication procedures in the

Federal Statistical Office where accomplished in March 2019 For the Truck-Toll-Mileage Index

the publication timelag is 15 days after the end of the reporting month and can possibly be

reduced to 7 days in the future

Furthermore the project analysed the statistical relationship between the Truck-Toll-Mileage

Index and Industrial Production Index in a descriptive way The non-seasonally adjusted values

show a strong correlation between the month-on-month rates of the two indices A clear

correlation can also be observed with domestic trade certain service sectors and the overall

economy However for the Industrial Production Index the decomposition of the data into time

series components has shown that the close relationship between mileage and production

applies especially to the seasonal and calendar components There is less accordance in mileage

and production when trend movements or irregular fluctuations are regarded The seasonally

adjusted result is composed of the trend component and the irregular component Therefore the

relationship between production and the mileage is less pronounced when looking at the

seasonally adjusted month-on-month rates of the indices An analysis of cyclical trend

movements however indicates that business cycle developments are certainly reflected in the

development of mileage in some cases showing clear accordance in the economic turning

points In the years 2015 and 2016 there is less coincidence structural changes are likely to play

a role here

The question of whether the Truck-Toll-Mileage Index can be used to estimate a mechanistic

nowcast for the Industrial Production Index has further been considered Such a nowcast should

refer to the seasonally adjusted month-on-month rate of the Industrial Production Index as this

figure is in the focus of the German press releases To proceed a nowcast estimation procedure

has been developed which is based on the seasonal adjusted month-on-month rates of the

indices Alternative approaches referring to unadjusted growth rates with subsequent seasonal

adjustment where rejected The nowcast method used is the in science widely recognized

RegARMA modeling method Seven regressors have been formed in order to be able to include

further information in addition to the Truck-Toll-Mileage Index as independent variables

Through various combinations of regressors nine different RegARMA models f1 to f9 were

developed which were tested against each other in the project For each of these models the

RegARMA procedure was applied rolling by extending the support span by one month and thus

adding one estimation in the estimation span In this way 48 single nowcasts were produced for

each monthly model A tenth model was added which refers to quarterly data accounting for the

3

fact that National Accounts may have an interest in a quarterly nowcast for the Industrial

Production Index

For each monthly model 48 errors were generated by subtracting the estimated growth rate from

the actually realised growth rate in the respective month The 10 time series of nowcast errors

where used to calculate various quality indicators which served to assess the quality of the

nowcast results for each model and which have before been developed within the project

The results for the quality indicators showed that none of the monthly models f1 to f9 is superior

in all quality indicators to the other For all models estimation errors that exceeded the median

of the absolute value of the month-on-month rates were observed frequently Also a false sign of

the growth rate where often estimated One further problem was the bias of the results which

could not be lessened by applying additional regressors as control variables Ultimately the

simple model f2 using the RegARMA approach with the Truck-Toll-Mileage Index as the only

regressor was identified as best model which produced a relatively good accuracy and showed

an only moderate bias The quarterly model produced worse results than the monthly models

In a further step the quality of the models f1 to f10 where compared to eight reference models

which have also been developed within the project The reference models produce mechanistic

nowcasts which disregard the information from the Truck-Toll-Mileage Index In its place for

example the Ifo Business Climate Index was used For monthly data it appears that the model f2

is almost consistently better in terms of precision than all reference models Regarding the bias

however all monthly reference models show better results than f2 For the quarterly models the

results of the Truck-Toll-Mileage Index cannot convince model RM7 based on the ifo Business

Climate Index is in all quality indicators at least as good as the results of f10 in the case of

quarterly data

So far the results give a first indication of the usefulness of the Truck-Toll-Mileage Index in

producing a nowcast for the Industrial Production Index The results of the simple mechanistic

nowcast calculations applied in this project have shown that for monthly data other indicators

do not lead to better results The fact that the Truck-Toll-Mileage Index is issued prior to the other

here discussed indicators makes the index a superior basis for mechanistic nowcast estimates

However a severe problem with biasedness of the results based on the Truck-Toll-Mileage Index

occurred which should be addressed in further research The project tested ten relatively simple

RegARIMA models More elaborated estimation methods could not be tested due to time

constraints Likewise the formation of regressors had to forego the integration of further

information for example on the traffic structure

The remaining question is whether the quality is sufficient enough to publish nowcasts in the

course of official statistics The fact that the model proposed in this project produces nowcast

errors which exceed the median of the absolute value of the target variable in 50 percent of all

cases at first advises against it In order to look for ways to improve the estimation models the

project results should be discussed with actors in Germany working in the field of economic

analyses The resulting improvement approaches must then be tested in further work steps

4

Contents Executive summary 2

Contents 4

List of abbreviations 5

1 Scope of the project 6

2 Explorative analysis of the relationship between mileage and production data 7

21 Calculation of the Industrial Production Index (IPI) 7

22 Description of the Truck-Toll-Mileage Index (MI) 9

23 Correlation of month-on-month rates for industrial production and mileage 11

24 Common cyclical developments for industrial production and mileage 13

25 Conclusions regarding the relationship between mileage and industrial production 15

3 Description of nowcast methods applied in this project 16

31 Formal description of the connection between MI and IPI 16

32 Support and estimation span 16

33 Handling of seasonal adjustment 17

34 Modelling of the nowcast calculation function 18

35 Software applied for the nowcast calculations 21

36 Quality indicators for assessing the nowcast results 21

4 Nowcast results and their interpretation 23

5 Reference models with other exogenous information 27

6 Conclusion 29

7 Implementation plan 30

Annex 30

5

List of abbreviations

BAG German Federal Office for Goods Transport

BC ifo Business Climate Index

CI smoothed ratio of capital and intermediate goods

CO smoothed ratio of non-durable and durable goods

CT RWIISL-Container Throughput Index

ESS European Statistical Systems

IPI Industrial Production Index

MAE mean absolute error

MaxAE maximum of absolute error

ME mean error

MI Truck-Toll-Mileage Index

MinAE minimum of absolute error

NSR noise-to-signal ratio

OI New Orders Index

Pcom proportion of estimates that fulfil PHM and PWS

PHM proportion of estimates where error higher than absolute median

PWS proportion of estimates with wrong sign

RMSE root mean squared error

TIM Theilrsquos inequality measure

TO smoothed ratio of non-domestic and domestic turnover

6

1 Scope of the project

On German motorways and national roads a toll for trucks is charged In the course of the toll

collection data on mileage (as driven kilometres) of the trucks is generated The toll collection

process works on the basis of the global positioning system (GPS data) Toll collection as well as

collection and processing of the thereby generated mileage data is done at the German Federal

Office for Goods Transport (BAG)

The BAG calculates a monthly mileage index (MI) which is a chronologically consistent indicator

of the kilometres driven by trucks on German roads The MI is available already 15 days after the

end of the reference month and with this is a very early statistic Currently it is being investigated

whether by the year 2020 a shortening of the timelag to about 7 days is possible Transport

services are in a national economy a requirement for and a consequence of the production of

goods Accordingly it turns out that the MI is highly correlated to the German Industrial

Production Index (IPI) calculated monthly in the Federal Statistical Office of Germany

The IPI measures changes in the volume of industrial output at monthly intervals It is a reference

indicator for economic research and is used in particular to identify turning points in economic

development at an early stage It is furthermore an important input statistics used in national

accounts for the quarterly extrapolation of the gross national product The IPI is one of the

earliest business cycle indicators in official statistics but still is issued with a timelag of 37 days

to the reporting month

The aim of the project described here is to evaluate whether the mileage index could be used to

calculate a monthly nowcast for the German IPI hence significantly shortening the timelag of the

IPI from 37 to 15 and later perhaps 7 days as well as accelerating the availability of input

statistics for national accounts The nowcast for IPI should refer to the seasonally adjusted

month-on-month rate of the IPI as this figure is in the focus of the German press releases (in line

with the general ESS guidelines on seasonal adjustment1)

The project is a feasibility study using statistical methods to decide whether the MI could be

used to continuously calculate a nowcast of the IPI in a ldquomechanisticrdquo way The term

mechanistic means that the nowcast should be able to be calculated automatically so that its

calculation should not require a resource intensive monthly expert judgment assessing all kind of

information available on the economic development The mechanistic nowcast calculation is

done by means of statistical procedures such as regressions and seasonal adjustment methods

In case of a positive result an adequate publication would take place for example a tendency

statement on the expected development of industrial production Another possibility would be to

publish the nowcast in an initial trial phase as so called ldquoexperimental statisticsrdquo on the

homepage of the Federal Statistical Office Currently an appropriate area for experimental

statistics is being set up in the Federal Statistical Office

1 Eurostat ESS guidelines on seasonal adjustment Luxembourg 2015 p 46

7

30

50

70

90

110

130

150

Jan 05 Jan 07 Jan 09 Jan 11 Jan 13 Jan 15 Jan 17 Jan 1930

50

70

90

110

130

150


2 Explorative analysis of the relationship between mileage and production data

In this chapter the statistical interrelationship between production and mileage (as driven

kilometres of tollable trucks) is presented The aim of the explorative analysis is to identify the

strengths and weaknesses of the mileage data in identifying turning points trends and current

developments This will help to develop an understanding of the underlying economic

relationship between production and mileage For this purpose it is first shown how the IPI and

the MI are calculated

21 Calculation of the Industrial Production Index (IPI)

Figure 1 provides an overview of the development of the non-seasonally adjusted Industrial

Production Index2 All charts refer to the same value range which for the sake of simplicity is only

indicated in the large graphics For the section of ldquoSpecialised construction activitiesrdquo results

are available only from January 2010 because this sector was not previously covered by the IPI

Figure 1 Non-seasonally adjusted Industrial Production Index from January 2005 to April 2019

Industry (incl energy and construction) Manufacturing

Intermediate goods

Capital goods

Consumer durable goods

Consumer non-durable goods

Energy Construction of buildings Civil engineering Special constr activities

The first step in the calculation of the Industrial Production Index is to compile monthly survey

results on the production development for about 5600 industrial products The survey results are

provided by local units of enterprises Depending on the type of product the value of production

in euros the quantities produced or in exceptional cases the turnover or hours worked in the

respective local units is applied for measuring the production developments As far as production

values or turnover are applied these figures are adjusted for price developments by dividing

production development by appropriate price indices (producer price indices building price

indices) When working hours are used productivity adjustments take place

2 In order to distinguish to seasonally adjusted results the non-seasonally adjusted results are referred to as unadjusted results

8

30

50

70

90

110

130

150


50

70

90

110

130

150


The development of production for the 5600 industrial products is then grouped into 246

subgroups corresponding to the classes (four-digits) of the NACE classification in most cases

Subindices are calculated in each class by dividing the current production value by the value in

the base year In a further step the subindices are combined into aggregates for higher levels A

headline index is calculated as weighted average of the associated subindices The weights are

calculated as gross value added at factor cost in the relevant sectors in the base year 2015 The

results of the IPI are generally published in the breakdown by economic activity in addition a

breakdown by main industrial groupings and construction sectors is available The publication

timelag is 37 days after the end of the reporting month

In the last step seasonal adjustment is conducted in order to filter out influences that regularly

occur at a similar rate over the course of the year and to make the cyclical and trend-based

economic development or unusual developments more transparent If necessary the procedure

also includes a calendar adjustment that calculates foreseeable calendar effects3 For seasonal

adjustment the mathematical-statistical method X13 and the ESS software application

JDemetra+ is used in the Federal Statistical Office of Germany Figure 2 shows the seasonally

adjusted results for the Industrial Production Index

Figure 2 Seasonally adjusted results for the IPI and subgroups from January 2005 to April 2019


Intermediate goods

Capital goods



Energy Construction of buildings

Civil engineering Specialised construction activities

For the later understanding of the construction of the nowcast it is important to know that at the

IPI the seasonal adjustment in the narrow sense is applied directly to the main industrial

groupings only the headline aggregates are adjusted indirectly by calculating a weighted

3 The term seasonal adjustment is used in this paper as a collective term for the adjustment of seasonal fluctuations and calendar effects Regarding seasonal adjustment by the Federal Statistical Office see Linz et al 2018b

9

average of the directly seasonally adjusted indices The aggregation scheme for the seasonal

adjustment of the IPI is shown in Table 1

Table 1 Aggregation scheme for the seasonal adjustment of the Industrial Production Index

Main industrial grouping or construction sectors

weight associated subindices examples

Industrial

Production

Index

= 100

Intermediate goods 2945 Manufacture of basic metals

Manufacture of paper and paper products

Parts of Manufacture of fabricated metal products except machinery and equipment

Capital goods 3698 Manufacture of motor vehicles trailers and semi-trailers

Manufacture of machinery and equipment nec

Parts of manufacture of computer electronic and optical products


Consumer durable goods 227 Manufacture of furniture


Parts of manufacture of electrical equipment

Parts of manufacture of other transport equipment

Consumer non-durable goods 1089 Manufacture of basic pharmaceutical products and pharmaceutical preparations

Printing and reproduction of recorded media

Parts of manufacture of food products

Manufacture of beverages

Manufacture of wearing apparel

Energy 637 Electricity gas steam and air conditioning supply

Manufacture of coke and refined petroleum products

Construction of buildings 201 Development of building projects

Construction of residential and non-residential buildings

Civil engineering 402 Construction of roads and railways

Specialised construction activities

801 Building completion and finishing

Electrical plumbing and other construction installation activities

22 Description of the Truck-Toll-Mileage Index (MI)

In Germany a distance-based toll for heavy goods vehicles (trucks) was introduced at the

beginning of 2005 The toll obligation initially applied to trucks with a gross vehicle weight rating

(GVWR) of 12 tonnes and above on the approximately 12800 km of federal motorways In the

ensuing years the toll obligation was successively extended and now applies to all trucks of 75

tonnes GVWR and above on all federal motorways and federal roads

The amount payable depends on the distance travelled on the tollable roads the number of axles

of a vehicle or vehicle combination and its emission class The German Federal Office for Goods

Transport performs the sovereign tasks regarding the implementation of the truck toll and

entrusted a private operator as agent with setting up and operating the toll collection system

Trucks on German federal motorways and roads have to log into the toll system for the settlement

of truck tolls Automatic log-in uses a vehicle device the so-called On-Board Unit (OBU) It uses

satellite signals to trace the distance travelled by the vehicle and transmits the data to the

operatorlsquos computer systems via mobile communication This log-in method was used for around

96 of all tollable journeys in 2017 When truck toll collection was introduced the Federal Office

for Goods Transport set up an information system which provides data on truck toll receipts

tollable journeys and mileage Figure 3 shows the development of toll road mileages since 2005

10

The vertical lines mark the dates of the toll extensions Reductions in tonnage limits and

extensions of the toll obligation to all federal roads led to significant increases in the tollable

truck mileage especially in 2018

Figure 3 Total monthly tollable mileage and toll extensions in billions of km

The attempt to relate the truck toll mileage to production developments is undermined by the fact

that the toll extensions affect the development of the truck toll data over time The German

Federal Office for Goods Transport therefore developed the ldquoTruck-Toll-Mileage Index rdquo (MI)

which excludes changes in the observed mileage from the time series that are caused by toll

obligation extensions This index represents the development of mileage as a fixed base index

for a subpopulation that can be observed continuously over time First the MI only includes the

mileage of trucks on federal motorways as the toll obligation where applied to all federal

motorways from the beginning Second only mileages of trucks with at least four axles are

included in the fixed base index since in most cases these are not affected by the toll extensions

related to GVWR in tonnes Heavy trucks were also affected by the toll from the start and usually

have four or more axles

Figure 4 Truck-Toll-Mileage Index and total mileage of all tollable vehicles values in the year 2005 equal to 100

Since the last extension of the toll obligation in July 2018 the MI has included an average of

around 72 of all toll mileages Up to autumn 2015 this share was between 90 and 95 Only

with the larger truck toll extensions the lowering of the tonnage limit and the extension to

11

30

50

70

90

110

130

150


unadjusted MI

seasonally adjusted MI

include all federal roads did the share decrease significantly Figure 4 shows the development of

the MI in comparison with the total tollable mileage For simplification both time series were

standardized to their average 2005 values The difference between the MI and the total tollable

mileage initially rises only slightly following the first extensions to the toll obligation with

significant differences only becoming apparent from autumn 2015

Much of the goods traffic on trunk roads can be assigned to the federal motorways meaning that

the MI provides a good indicator of total road freight transport The index is calculated at the

German Federal Office for Goods Transport and provided monthly to the German Federal

Statistical Office for seasonal adjustment and final publication as standalone business cycle

indicator The seasonal adjustment and publication procedures in the Federal Statistical Office

have been developed within this project together with the Federal Office for Goods Transport and

where accomplished in March 20194 For the Truck-Toll-Mileage Index the publication timelag is

15 days after the end of the reporting month and can possibly be reduced to 7 days in the future

The seasonal adjustment method is again X13 in JDemetra+ Figure 5 shows the development of

the unadjusted and seasonally adjusted Truck-Toll-Mileage Index from January 2005 onwards

The base year of the MI is currently 2015 and updated every 5 years

Figure 5 Unadjusted and seasonally adjusted results for the MI from January 2005 to May 2019

Additional information of the Truck-Toll-Mileage Index and the cooperation between the German

Federal Statistical Office and the Federal Office for Goods Transport can be found in the paper in

Annex A of this project report It is the English translation of a joint essay published in German

language by the Federal Statistical Office in December 2018 in cooperation with the German

Federal Office for Goods Transport

23 Correlation of month-on-month rates for industrial production and mileage

The left side of Figure 6 shows the month-on-month rates of the non-seasonally adjusted

production index for manufacturing on the Y-axis and the corresponding changes in the Truck-

Toll-Mileage Index on the X-axis It turns out that the statistical interrelation of the unadjusted

Industrial Production Index for manufacturing and the unadjusted Truck-Toll-Mileage Index is

reasonable high

4 See press release from 1432019 in German and English on httpswwwdestatisdeENPress201903PE19_096_421html

12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057

Figure 6 IPI for manufacturing and MI Month-on-month rates of the unadjusted and seasonally adjusted indices percent

correlation coefficient r and regression line

The correlation coefficient of 085 indicates a tight relationship between monthly production and

truck mileage in Germany Raw materials and intermediate products have to be transported to

the production sites and industrial products have to be delivered to the customers Freight

services may therefore occur before during or after production In many areas of industry

delivery production and transport are closely interwoven in just-in-time supply chains The

analysis of time series shifts has shown that the relationship is strongest between the Industrial

Production Index and the mileage index for the same period ndash the relationship is significantly

weaker when production comparing the index with the mileage in previous or subsequent

months The Annex A contains analogous charts for the subindices of the Industrial Production

Index broken down into main industrial groupings It should be noted that for the MI no

breakdown by main groupings or other industries are possible the MI always refers to the overall

economy and a breakdown of mileage for example according to types of goods to be transported

is not possible with the truck-toll data As can be seen in Annex A the statistical interrelation is

especially high for intermediate goods (r=097) and consumer non-durables (r=091) For

consumer durables (r=080) and especially for capital goods (r=063) the statistical correlation

between the non- seasonally-adjusted monthly rates of change of the indices is less pronounced

In summary with regard to the unadjusted data a strong statistical relationship can be observed

between production in manufacturing and mileage varying in strength between the various

sectors Similar results can be seen when comparing the month-on-month rates of the turnover

index (Annex B page B15 et seqq) or the German New Orders Index for manufacturing (Annex B

page B38 et seqq) although the relationship is strongest for the Industrial Production Index A

clear statistical relationship between mileage and economic activity could also be observed for

domestic trade sectors (such as wholesale trade motor vehicle trade) the latter being measured

by turnover Furthermore a clear statistical relationship can be measured between the quarterly

mileage and the quarterly rates of change of the gross domestic product from the national

accounts Finally as expected the quarterly turnover in the ldquoFreight transport by road and

removal servicesrdquo sector also correlates with the mileage

In time series analysis the time series are broken down into different components Typically

these are the seasonal component the calendar component a trend-cycle component and an

irregular component

Unadjusted Calendar and seasonally adjusted

13

The further investigation of the statistical correlation for the field of intermediate goods has

shown that the relationship in the seasonal and calendar component is particularly strong

(r=098 and 099 respectively) For the trend-cycle component (r=085) and the irregular

component (r=036) the relationship is somewhat less strong (see Annex A) Now the seasonally

adjusted result is composed of the trend-cycle and the irregular component while the seasonal

and calendar components are eliminated Conversely the strong accordance between

unadjusted IPI and MI means that the use of seasonal adjustment excludes a significant degree

of covariance between the two indices from the data The right side of Figure 6 shows the month-

on-month rates of the calendar and seasonally adjusted indices in a scatterplot The correlation

coefficient is 057 which is significantly lower than in the non- seasonally adjusted time series

The irregular component plays an important role in business cycle analysis as trend changes can

be detected the earliest on movements in the irregular component For example a sudden

economic downturn may initially appear as an irregular movement and only later be recognized

as a permanent trend downturn The irregular component comprises both random and

economically explicable influences which have a short-term effect and which do not belong to

the other components ndash such as the effects of strikes on production within an industry In

production for example irregular movements can occur due to technical disruptions in the

production processes in the establishments or due to unusual holiday constellations In the case

of truck mileage traffic restrictions on larger stretches of road due to major roadworks or snow

and icy roads can lead to irregular movements for example Official statistics provide little

information on the relevance frequency and impact of such events this information cannot be

gathered because of the burden on respondents5 Some of the influencing variables such as

lengthy strikes could affect both production and mileage In many cases however there are

presumably different causes of irregular fluctuations in production and mileage or common

causes of fluctuations are reflected differently in production and mileage The lower statistical

interrelation in the irregular component seems to reflect this

24 Common cyclical developments for industrial production and mileage

Cyclical economic movements can be presented for example as deviations of a medium-term

trend from the long-term growth path of a time series The Federal Statistical Office uses the

BV41 method to calculate medium-term trends 6 It is particularly suitable for mapping economic

movements that span three or more years At the same time it smooths out intra-annual

fluctuations to a considerable extent Cyclical economic movements can be presented in

isolation by examining the deviation of a BV41 trend from its long-term growth path Figure 7

shows the cyclical developments of the Industrial Production Index for manufacturing and for the

Truck-Toll-Mileage Index

5 The ifo Institute for Economic Research also gathers rough appraisals of the relevance of certain impediments to production in its manager surveys Managers are asked for example whether production in their own company has been hindered in the current month through a lack of raw or intermediate materials or through insufficient technical capacity However such questions are only included in the questionnaire on a quarterly basis A comparison with the irregular components of the production index for intermediate goods and the truck mileage index aggregated to quarterly results reveals little accordance 6 Speth Hans-Theo Komponentenzerlegung und Saisonbereinigung oumlkonomischer Zeitreihen mit dem Verfahren BV41 In Statistisches Bundesamt (editor) Methodenberichte Issue 3 2004 [Accessed on 22 October 2018] Available at wwwdestatisde

14

-015

0

015


tre

nd

de

via

tio

n in


Industrial Production Index

Figure 7 Economic cycle as deviation of medium-term trend from long-term trend

Industrial Production Index for manufacturing and Truck-Toll-Mileage Index

A review of the entire time series reveals several examples of precise accordance between the

economic turning points at other points however there are divergent developments For both

time series the downward movement caused by the economic and financial crisis begins

simultaneously in February 2008 with both dips coinciding in July 2009 The decline caused by

the euro crisis appears two months earlier in the MI than in the Industrial Production Index for the

manufacturing sector ndash while the dip occurs at exactly the same time in both time series the

peaks of the subsequent recovery also coincide In the years 2015 and 2016 the development of

the mileage index seems to have decoupled itself from the development of production this

period is characterised by less pronounced cyclical movements in the Industrial Production

Index A common turning point can be observed again at the turn of 20172018 This appears in

the mileage index only one month earlier

When interpreting the common economic cycle it should be noted that mileage and production

are very different variables The truck mileage indicates the total distance travelled it contains no

information on the monetary value and is only indirectly related to the quantity of goods

transported The Industrial Production Index on the other hand refers to monetary variables and

its purpose is to show the development of the total value of goods produced at constant prices

The statistical relationship between mileage and production which is nevertheless clearly

visible can be influenced by structural changes in industrial demand for freight services For

example it is noted that increasing volumes of higher-value goods are being transported an

increasing proportion of which by road and involving longer transport distances The transport of

bulk goods by contrast is declining7 Higher-value goods are to be found for example in the

main industrial grouping of consumer durables Figure 8 shows the Industrial Production Index

cycle for consumer durables alongside the cyclical development of the Truck-Toll-Mileage Index

There was a joint upward tendency in 2015 and 2016 with both the production of consumer

durables and road freight transport increasing during this period The increase in this industrial

production sector is scarcely reflected in the Industrial Production Index for total manufacturing

as this main industrial grouping only accounts for roughly 3 of the total index

7 See SSP Consult Gleitende Mittelfristprognose fuumlr den Guumlter- und Personenverkehr Mittelfristprognose Winter 20172018 WaldkirchKoumlln February 2018 [Accessed on 22 October 2018] pp 31 and 38 Available at httpassetsbmede

15

-015

0

015


tre

nd

de

via

tio

n in




Industrial Production Index for consumer durable goods and Truck-Toll-Mileage Index

Other possible factors that could have influenced the growth in mileage from 2015 on include the

increasing sales of German industrial companies to euro area countries For example the

industrial turnover index shows that sales posted by German industrial companies to the euro

area countries have risen significantly since around 2014 but this is not reflected in the

Industrial Production Index the destination of the goods produced is not taken into account in

the calculation of the Industrial Production Index

25 Conclusions regarding the relationship between mileage and industrial production

The non-seasonally adjusted values show a clear statistical relationship between the production

and truck-toll-mileage indices Much of this is probably attributable to common seasonal

movements Regular intra-annual fluctuations in production may impact on truck mileage as the

result of production company demand for freight transport in some cases factors such as typical

annual weather fluctuations may have a similar effect on production and freight traffic The

strong similarities in both the seasonal pattern and the calendar effect imply that applying

seasonal adjustment methods excludes some of the covariance from the data The irregular

movements as part of the seasonally adjusted time series reveal less accordance between

production and mileage development In road freight transport and the production of

intermediate goods there would appear to be few common causes of the exceptional short-term

influences or they have very different effects on the two variables Seasonally adjusted results

which play an important role in the analysis of recent economic developments also show a

correlation between mileage and industrial production However this is significantly lower than

in the non-adjusted figures The cyclical course of economic activity measured by the deviation

of medium-term developments from the long-term trend reveals a number of common

developments particularly at the economically relevant turning points However structural

changes in industrial activity may be relevant which are reflected in the Truck-Toll-Mileage Index

but not in the Industrial Production Index for manufacturing Overall there is a clear statistical

relationship between the Truck-Toll-Mileage Index and various short-term statistics in particular

the Industrial Production Index

16

3 Description of nowcast methods applied in this project

As described above the final aim of this project is to employ the Truck-Toll-Mileage Index for

nowcasting the month-on-month rates of changes of the seasonally adjusted IPI For this

purpose the chosen approach of the estimation procedure is described and different models for

estimation are introduced in this chapter In order to evaluate the suitability of different

statistical nowcasting models objective statistical measures for the nowcast quality has been

developed which are also described in this chapter

31 Formal description of the connection between MI and IPI

The nowcasts developed in this project are based on the estimation of growth rates These are

calculated as month-on-month rates (m-o-m) of seasonally adjusted index numbers with the

following equation8

(1) Yt = I t

Itminus1minus 1

I hellip seasonally adjusted value of the IPI

Y hellip growth rate of seasonally adjusted IPI

t hellip time period

The following terminology is introduced for the description of the nowcast models Y denotes the

actual realisation of the growth rate of the IPI (the index to be nowcasted) and Y for the

nowcasted growth rate of the index The nowcast is calculated as a function of one or several

independent variables

(2) YtSA = fi(x1t x2t hellip )

YSA hellip nowcasted seasonally adjusted growth rate of the IPI

X hellip seasonally adjusted growth rate of the truck toll mileage-index

and possibly other independent variables

fi hellip calculation function as estimated for model i

The approach implies that the nowcast for the period t can only be calculated after the

independent variables are available for the same period t Therefore the most important

independent variable in this project is the Truck-Toll-Mileage Index this index is available with

the lowest timelag within official short-term statistics in Germany However other independent

variables which occur later can be added as will be described below as lagged variables which

refer to the period t-1 The independent variables will normally be inserted as growth rate of an

index but here also alternative solutions are possible and will be discussed below

32 Support and estimation span

For the Truck-Toll-Mileage Index monthly results are available from January 2005 hence growth

rates can be computed for February 2005 onwards In order to develop the functional relationship

fi between the growth rates of MI and the IPI nine different models have been tested for monthly

data Each of these nine models has been applied to 48 repeated nowcast calculations as shown

in figure 9

8 Supplementary a nowcast estimate for the Industrial Production Index based on quarterly data was also carried out in this project In this case the growth rates in equation (1) are quarter-on-quarter rates (q-o-q)

17

Figure 9 Time spans of the 48 nowcast calculations

In the first of these 48 calculations a support span from February 2005 to December 2014 has

been applied to calculate the nowcast for January 2015 In the next estimation the support span

has been extended until January 2015 to calculate the nowcast for February 2015 and so on

Ultimately the calculations are based on support spans ranging from 119 to 166 observations

and the resulting nowcasts refer to a period of 48 months reaching from January 2015 to

December 20189

It should be noted that neither the time series of the independent variables nor of the dependent

variable for the IPI has been introduced as real time data vintages In a more realistic data

vintages view due to data revisions the overlapping part of the 48 support span data sets will

slightly differ from month to month In order to limit the calculation effort the influence of data

revisions was not considered here All data used in the project refer to the data release date of

May 2019

For each month of the estimation span both estimated results (Y) and actual results (Y) for the

IPI are available so that they can be compared with each other The data can be used to calculate

nowcast errors E for each of the 48 observations which are defined as

(3) Et = Yt minus Yt

Et hellip Nowcast error in period t

The set of nowcast errors will be used to evaluate the quality of the tested model and to select

the most promising functional modelling

33 Handling of seasonal adjustment

Instead of estimating the growth rate of the seasonally adjusted index it would theoretically also

be possible to estimate the growth rates of the unadjusted nowcast and to seasonally adjust it

subsequently This approach is particularly interesting against the background that seasonal

adjustment can have a major impact on the results Seasonal adjustment includes an estimation

of seasonal factors which are then applied to unadjusted indices The estimation of seasonal

factors require various assumptions to be made and parameters to be set and the results often

react strongly even to small changes of the unadjusted values Therefore it would be

9 For the model with quarterly periodicity the monthly data is aggregated The support spans range from 39 to 54 quarters and the estimation span contains 16 quarters The estimation procedure is conducted analogue to those models with monthly periodicity

Y120

Y119

Y1 hellip Shortest support span

119 observations Feb2005 ndash Dec2014

Estimation span 48 observations

Jan2014 ndash Dec2018

Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip

Largest support span 166 observations Feb2005 ndash Nov2018

18

advantageous if the same seasonal factors could be applied to the nowcast as they are applied

to the actual index The following equations refer to the handling of seasonal adjustment in the

process of nowcast estimation The superscript SA denotes the seasonally adjusted results while

the superscript U represents the unadjusted values Two options exist in calculating a seasonally

adjusted nowcast

(4) Option A YtSA = f SA(x1t

SA x2tSA hellip )

(5) Option B YtSA = Yt

U minus ( st

stminus1minus 1) where Yt

U = f U(x1tU x2t

U hellip )

YtSA hellip nowcasted seasonally adjusted growth rate of the index

x1tSA hellip growth rate of seasonally adjusted independent variable

Ytu hellip nowcasted unadjusted growth rate of the index

x1tu hellip growth rate of unadjusted independent variable

st hellip seasonal factors (including eventual calendar effects)

In option A a seasonally adjusted nowcast is produced by applying the functional relationship fSA

to growth rates of seasonally adjusted independent and dependent variables In option B an

unadjusted nowcast is produced by applying the functional relationship fU to growth rates of

unadjusted independent and dependent variables The resulting growth rate is then seasonally

adjusted by subtracting the growth rate of the seasonal factors from the growth rate of

unadjusted growth rate10 In option B the seasonal factors in equation (5) would be the same as

those used to calculate the official seasonally adjusted Industrial Production Index In practice

however the approach B is not possible Although seasonal factors are estimated in advance on

pile they can be modified from month to month in the case of a ldquocontrol currentrdquo seasonal

adjustment regime11 The final seasonal factor for month t is therefore only available after

compiling the official Industrial Production Index for month t Even if the problem of

modifications is neglected the seasonal factors which are needed for the seasonal adjustment in

equation (7) do not exist in the day-to-day business As described in paragraph 11 the

Industrial Production Index is obtained by aggregating seasonally adjusted subindices for main

industrial groupings Thus seasonal factors are available on the level of main industrial

groupings only not for the headline Industrial Production Index Hence option B is not pursued

in the further

34 Modelling of the nowcast calculation function

For the modelling of the calculation function f in this project a RegARMA approach has been

applied It is based on a regression function which is combined with ARMA time series models12

In the following equation the approach is indicated

(6) Yt = prop∙ X1t + β ∙ X2t + ⋯ + Zt with Zt ~ ARMA process

ARMA models contain two different modeling options a modeling of autoregressive processes

and of so-called moving average processes These processes refer to the respective preceding

10 Typically unadjusted indices are divided by the seasonal factors in order to calculate the seasonally adjusted result The seasonal adjustment of growth rates therefore requires that the change in seasonal factors be subtracted from the growth rate of the index 11 See ESS guidelines on seasonal adjustment (2015) p33 ldquoForecasted seasonal and calendar factors derived from a current adjustment are used to seasonally adjust the new or revised unadjusted data However an internal check is performed against the results of the ldquopartial concurrent adjustmentrdquo which is preferred if a significant difference exists This means that each series needs to be seasonally adjusted twiceldquo 12 See BoxJenkins (1970) for ARIMA models in time series analysis An easy-to-read and application-oriented introduction is given by Nazmen (1988)

19

periods On the whole two specification parameters are used for the presentation of the regular

ARMA processes which are shown as ARMA(pq) The parameter p and q denote the order of the

autoregressive process and the order of the moving average process and usually appear only

with the values zero or one The parameters of the ARMA models are chosen according to an

automatic optimizing procedure referring to the Akaike information criterion based on

estimations of the likelihood of a model to fit the future values (in-sample fit)

In the RegARMA model different regressors can be used as explanatory variables The regressors

that are considered in this project are denoted in Table 2

Table 2 Regressors applied in the project

Notation Regressors Timelag

MI Truck-Toll-Mileage Index 0 months

BC Business-cycle index for manufacturing 1 month

OI New Orders Index for manufacturing 1 month

CT RWIISL-Container Throughput Index 1 month

TO 13-month moving average of the ratio non-domestic domestic turnover in manufacturing Current value is computed by asymmetric filter

0 months

CI 13-month moving average of the ratio production of capital intermediate goods Current value is computed by asymmetric filter

0 months

CO 13-month moving average of the ratio sales of durable non-durable goods Current value is computed by asymmetric filter

0 months

The first independent variable is always the growth rate of the truck toll mileage-index (MI) The

following additional regressors where occasionally used in this project

The regressor BC denotes the growth rate of the ifo Business Climate Index which is a widely

observed early sentiment indicator for economic development in Germany It is based on monthly

survey amongst company managers which are asked to give their assessments of the current

business situation and their expectations for the next six months In this project the subindex

referring to survey responses of enterprises in manufacturing where applied As the ifo Business

Climate Index is released about ten days after the MI it can only be introduced as lagged

information into the regression equation thus referring to the previous reporting month t-1

Given that the ifo business cycle index includes expectations using the lagged variable may also

partly carry information about the current reporting month

The regressor OI stands for the growth rate of the German New Orders Index which measures the

monthly development of the deflated value of new orders in enterprises in selected branches of

manufacturing Per definition new orders are all orders definitely accepted by the

establishments in the reference month The German New Orders Index is released one or two

days before the Industrial Production Index and can therefore only be introduced with a timelag

of one month into the regression equation for the nowcast Again as new orders may indicate

subsequent production using the lagged variable may also partly carry information about the

current reporting month

20

The regressor CT represents the growth rate of the RWIISL-Container Throughput Index which

was developed by the RWI Institute of Shipping Economics and Logistics (ISL) and which aims at

providing timely information on short term trends in international trade The index is based on

the consideration that containers have become the most important means of transporting

international products Analogue to the ifo Business Climate Index it is released about ten days

after the MI and is introduced as lagged variable referring to the reporting period t-1

The three repressors TO CI and CO serve to control structural changes in the relationship

between industrial production and mileage in the transport of goods by road The regressor TO

denotes the relationship between domestic and non-domestic sales of German industry Here it

is assumed that increased foreign sales do ceteris paribus come along with freight being

transported over longer distances ndash without an increase in production The connection between

freight transport and production is thus changed when the relationship between domestic and

non-domestic sales changes CI reflects the relationship between produced capital and

intermediate goods Here it is assumed that freight transport of capital goods rather takes place

on streets than the freight transport of intermediate goods A shift in the production of

intermediate goods to the production of capital goods could increase the total road mileage more

than the overall production Again the connection between freight transports and production

may be affected The regressor CO serves to control for the relationship between the production

of consumer durable and consumer non-durables Analogue to previous case it is assumed that

durable goods need more freight transport services than non-durables

Combining the ARMA modelling with the above mentioned repressors the following ten

RegARMA-models where tested in this project

Table 3 Nowcast models based on RegARMA

notation regressors ARMA term frequency

f1 X1 MI ARMA(00) monthly


f3 X1 MI X2 BC ARMA(11) monthly

f4 X1 MI X2 OI ARMA(11) monthly

f5 X1 MI X2 TO ARMA(10) monthly

f6 X1 MI X2 CI ARMA(10) monthly

f7 X1 MI X2 CO ARMA(10) monthly

f8 X1 MI X2 TO X3 OI ARMA(11) monthly

f9 X1 MI X2 TO X3 OI X4 BC ARMA(11) monthly

f10 X1 MI ARMA (10) quarterly

The first model f1 is a simple linear regression of the MI on the Industrial Production Index The

simple linear model is a special case of RegARMA models with autoregressive and moving

average parameters set to zero meaning that the estimation error follows a white noise process

21

The second model f2 is a regression of the MI where the regression errors follow an

autoregressive process of order 1 Hence in addition to the information about the MI the model

f2 considers the time dependency of the growth rate of the IPI in month t to its growth rate in

month t-1 The models f3 to f9 are extensions which add the additional regressors described

above to the Truck-Toll-Mileage Index The ARMA-parameters are as always optimised according

to the Akaike criterion as described above The last model f10 considers the relationship

between production and mileage on a quarterly basis It was introduced because in the German

National Accounts the IPI is used to update the quarterly domestic product The national

accounts can thus be considered as users of the IPI As part of their own Nowcast projects they

have an interest in a quarterly nowcast for the German Industrial Production Index

35 Software applied for the nowcast calculations

Since ten models for the functional relationship f had to be tested in this project it was

necessary to use software that can automate the nowcast calculation procedure described

above The suitability of the software JDemetra+ was considered at this point This software

provides a plug-in that could have been used to estimate the models presented above However

the plug-in does not currently provide a way to automate the nowcast calculation procedure so

that for each model 48 individual calculations would have to be performed one by one ndash which

would have resulted in a very high calculation effort of 480 single estimations Therefore the

nowcast calculations have in this project been calculated with the software R Here the functions

lsquoARIMArsquo and lsquoforecastrsquo from the package lsquoForecastrsquo by Rob Hyndman supply a fully automated

forecast procedure and are there used to specify the ARMA models and estimate the forecasts13

36 Quality indicators for assessing the nowcast results

As described above the monthly results from the estimation span can be used to estimate a

series of 48 nowcasts for each model f1 to f10 The 10 time series of nowcast errors can be used

to calculate various quality indicators which can be used to assess the quality of the nowcast

results for each model Such an evaluation depends ultimately on the observerrsquos loss function

ie on how one rates deviation of the nowcasts from the actual realisation Table 4 provides an

overview of the quality indicators Q1 to Q10 which can partly be interpreted as concrete formal

expressions of loss functions

Q1 The mean absolute error (MAE) assumes a linear loss function as all errors are weighted the

same disregarding both the time and the extent of the nowcast errors Et

Q2 With the root mean squared error (RMSE) a quadratic loss function is assumed It punishes

large deviations of the nowcast from the actually realised value harder A high value of RMSE

compared to MAE suggests that some few mispredictions make a large contribution to the

forecast error RMSE is a common and often applied quality measure for forecasts

Q3 and Q4 The indicators MinAE and MaxAE mark the ends of the error distribution in absolute

terms and thus give an impression of its range

13 Hyndman RJ (2017) Forecast Forecasting functions for time series and linear models R package version 82 URL

httppkgrobjhyndmancomforecastgt

22

Q5 Estimates are judged in part according to whether they correctly anticipated the sign of the

growth rate to be estimated Therefore PWS indicates the percentage of estimates that estimated

the wrong sign of the growth rate

Table 4 Quality indicators for assessing the results of the nowcasts

Q1 MAE mean absolute error MAE = 1

Tsum |Et|

T

t=1

smaller is better

Q2 RMSE root mean squared error RMSE = radic1

Tsum Et

2T

t=1

smaller is better

Q3 MinAE minimum absolute error MinAE = min(|119864119905|) smaller is

better

Q4 MaxAE maximum absolute error MaxAE = max(|Et|) smaller is

better

Q5 PWS proportion of errors with wrong sign

PWS = sum s

T∙ 100 where s=1 if

Yt

Ytlt 0 else s=0

smaller is better

Q6 PHM proportion of errors higher than

median of 119884119905 PHM =

sum h

T∙ 100 where h=1 if Et gt m else h=0

smaller is better

Q7 Pcom

proportion of errors with wrong sign and errors higher than

median of Yt

Pcom = sum z

T∙ 100 where z = 1 if Et gt m and

Yt

Ytlt

0 else z = 0

smaller is better

Q8 NSR noise-to-Signal Ratio NSR =

1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better

Q9 ME mean error ME = 1

Tsum Et

T

t=1

small in absolute figures is

better sign gives

indication for under-

overestimation

T hellip number of observations in the estimation span (T = 48)

m hellip median of absolute value of Yt in the estimation span (monthly m = 082 percent quarterly m = 081 percent)

Q6 The indicator PHM provides the proportion of errors which are severely high It counts errors

being higher in absolute terms than the median of the absolute realised growth rates of the

Industrial Production Index within the estimation span In this project this median is 082

percent for the monthly time series and 081 for the quarterly time series Thus PHM counts

nowcast errors being higher than 082 (081) percentage points in models with monthly

(quarterly) frequency

Q7 The indicator Pcom gives the proportion of estimates where the sign was wrongly estimated

and the errors exceed the median growth rate Hence it is a combination of Q5 and Q6

Q8 The noise-to-signal ratio takes into account the fact that equally large nowcast errors weigh

more heavily in less volatile series than in series which themselves fluctuate very strongly The

NSR should be less than 1 If this is the case the nowcast reduces the uncertainty regarding the

estimation of the current development as it results from the deviation of the nowcasts in the

past

Q9 The indicator provides a measure for biasedness Nowcasts lead to both overestimations and

underestimations which in case of unbiasedness more or less cancel out each other The simple

23

mean error would be near zero in this case Positive or negative values for NSR however indicate

a bias in the estimation results

4 Nowcast results and their interpretation

The following table contains the accuracy measures described in chapter 26 for the models f1 to

f10 In figure 10 below the standardized nowcast for the monthly models f1 to f9 are compared

in a cobweb diagram14

Table 5 Quality results for nowcast models

f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075

f1 The first model f1 is a simple linear regression of the Truck-Toll-Mileage Index on the

Industrial Production Index The mean absolute error (MAE) at 087 is slightly higher than the

median of the absolute amounts of the month-on-month rates of the Industrial Production Index

over the estimation span (the median of the absolute rates is 082) The root mean squared error

(RMSE) is with 105 higher than the mean absolute error MAE which points in the direction that

some high errors affect the results This is also indicated by the fact that the range of estimation

errors for model f1 ranges from nearly zero to 224 percentage points The percentage of

estimates that estimated the wrong sign of the growth rate (PWS) is at more than one third PHM

shows that for around 52 percent of all observations the estimation error is higher than the

median of absolute rates False signs combined with errors accounting for more than the median

of absolute rates (Pcom) occur in about 10 percent of cases So in 10 percent of the nowcasts it

was not just the rates of change that are close to zero where the sign was misjudged The noise-

to-signal ratio (NSR) is at 056 indicating that the uncertainty regarding the estimation of the

current development can be reduced by applying this model With -016 ME indicates a

moderate bias of the estimation

f2 In the next step the model was enlarged by an ARMA-process Most accuracy measures

indicate that the RegARMA regression f2 is superior to the simple regression f1 except that

MaxAE is slightly higher in f2 However f2 is noticeable more biased ME is -029 for f2 whereas

before it was at -016 This means that the estimated nowcast is on average 029 percentage

14 Each quality measure was standardized by subtracting its mean and dividing through its standard deviation over the models f1 to f9

24

points higher than the true realisation which leads to a significant overestimation of the

Industrial Production Index Possibly the trend decoupling mentioned in chapter 2 from 2015

onwards cannot be adequately reflected in this model meaning that the Truck-Toll-Mileage Index

yields more growth than the Industrial Production Index

f3 The previous model f2 has further been extended in f3 by the lagged ifo Business Climate

Index Some quality indicators have improved in this model due to the additional information

although the span of measuring errors increases (Max AE is at 266 compared to 236 in f2) The

accuracy measures MAE and RMSE and the NSR decrease slightly Especially the proportion of

wrong signs has fallen from nearly 29 percent in f2 to less than 23 percent in f3 PHM has also

shrunk slightly from 50 to less than 48 percent The combined measure Pcom remains at 625

percent The disadvantage of this model is mainly that the bias has again increased from -029 in

f2 to -036 in f3

f4 In this model the ifo Business Climate Index has been replaced by the New Orders Index The

results are however not much better than in the previous model f3 The proportion of wrong signs

(PWS) is inferior to f3 whereas PHM improves significantly from about 48 to 40 percent of cases

with errors higher than median of absolute growth rates Pcom remains again at 625 percent

Unfavorable is that the bias has increased even further and is now at -043 percentage points

f5 Model f5 includes the regressor TO referring to the ratio between non-domestic and domestic

turnovers as additional variable beside the Truck-Toll-Mileage Index The idea is to control for

structural changes in the development in sales direction and thereby decrease the bias The ifo

Business Climate Index and the New Orders Index were not used here Comparing this model to

f2 it turns out that the ME actually increased from -029 in model f2 to -03 in f5 Also PWS and

Pcom increase in f5 compared to f2 However model f5 stands out by the best results in the

quality indicator RMSE

f6 Model f6 includes the regressor CI describing the ratio of capital and intermediate goods as

control variable In terms of biasness model f6 performs relatively well ME is at -026 In

comparison to model f2 most accuracy measures perform better except for the sign indicator

PWS and thus also for Pcom However if one compares f6 with f5 most quality measures are

worse however the bias is a bit lower for f6

f7 Model f7 inserts the regressor CO referring to the ratio of durable and non-durable goods as

control variable Comparing this model to f2 the bias measured by ME decreases from -029 in

model f2 to -027 in f7 For the models containing control variables f7 has lowest Pcom of 833

percent However the Pcom in f2 is still better with 625 percent Also for PWS f2 yields 292

percent this number cannot be improved by f7 or any model containing a control variable In

contrast MAE and RMSE are superior to f2 even if the results in f7 are not better than those of

the other models with control variables

In the following it is investigated whether a combination of the models f3 or f4 (Truck-Toll-

Mileage Index with ifo Business Climate or with New Orders Index) with the control variable in

model f5 (variable TO reflecting the ratio between non-domestic and domestic turnovers) can

improve the nowcast The control variable of f5 was chosen because model f5 was characterized

by the smallest RMSE and the smallest PHM within f5 f6 and f7

25

f8 Model f8 tests whether complementing the New Order Index by the control variable TO yields

good results The quality measures MAE and RMSE do not improve much in f8 compared to f4

The proportion of wrong signs PWS and Pcom yield equivalent results as f4 However

noticeable is that the ME increased to -042 which is the highest bias in all monthly models

f9 Model f9 complements model f8 by the ifo Business Cycle Index In comparison to f8 the

RMSE and PWS improved The RMSE decreases slightly from 103 in f8 to 101 in f9 and the PWS

decreases from 25 percent to less than 23 percent The quality indicator MAE and Pcom remain

unchanged even though PHM increases significantly PHM yielding about 54 percent is the

highest and hence worst value given all monthly models The bias measured by ME improved

slightly it decreased from -042 in f8 to -038 in f8

Figure 10 Standardized quality results for monthly nowcast models

So far in table 5 and in figure 10 one can see that none of the models f1 to f9 is superior to the

other models in all quality measures Some indicators hardly vary across the models

In order to select one final model a pragmatic approach could be to focus on the quality

measures Pcom and ME When publishing the results of the nowcast of the index it would be of

great relevance to avoid large errors with wrong signs Pcom precisely accounts for it This quality

measure puts preference on models that nowcast the correct direction of this index by punishing

large errors with wrong signs Similarly it is also important to rely on a quality measure such as

ME which provides indication for the direction and magnitude of the bias ie under- or

overestimation This information could not be obtained for instance from MAE or RMSE given

the fact that the error is positively normalized by the loss functions of these measures

For Pcom models f2 f3 f4 f8 and f9 are characterised by the smallest values Among these

models the model f2 is less biased Ultimately therefore the model f2 a RegARMA regression

with the Truck-Toll-Mileage Index as the only regressor seems the most suitable At the same

time due to its simplicity this model has the advantage that it can be calculated easily and

efficiently In figure 11 the development of the Industrial Production Index is shown together

with the nowcast estimates realised with model f2 in the estimation span from January 2015 to

MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26

December 2018 In chapter 4 the results of model f2 are compared with those of various

reference models that calculate a nowcast without the Truck-Toll-Mileage Index

Figure 11 Industrial Production Index for manufacturing and its nowcasts with model f2

f10 The last model is a quarterly RegARMA regression of the Industrial Production Index on the

Truck-Toll-Mileage Index As mentioned above a quarterly nowcast of the Industrial Production

Index can be of interest for national accounts and is therefore also tested in this project (The

quality indicators for the quarterly model cannot be compared to those of the monthly models f1

to f9) The mean absolute error MAE is in f10 with 109 significantly higher than the median of the

absolute amounts of the quarter-on-quarter rates of the Industrial Production Index over the

estimation span the median of the absolute quarter-on-quarter rates is 081 The proportion of

errors higher than the median is at 50 percent The percentage of estimates that estimated the

wrong sign of the growth rate (PWS) is at more than 40 percent False signs combined with errors

accounting for more than the median (Pcom) occur in about six percent of cases

75

90

105

Jan 05 Jan 07 Jan 09 Jan 11 Jan 13 Jan 15 Jan 17


nowcasts with model f2

27

5 Reference models with other exogenous information

This chapter examines the quality of a mechanistic nowcast if the information from the Truck-Toll-

Mileage Index were not available For this purpose reference models are calculated which are

specified without the MI The reference models applied in this project are listed in Table 6

Table 6 Overview of reference models

RM1 AR1 AR1 process without exogenous regressor (10) no regressor

RM2 AltSynXc ifo Business Climate Index Manufacturing (10)

regressor applied concurrently

RM3 AltSynXs RWIISL-Container Throughput Index (00)

RM4 AltLagXc ifo Business Climate Index Manufacturing (10)

regressor applied as lagged variable RM5 AltLagXs RWIISL-Container Throughput Index (00)

RM6 AltLagXo New Orders Index Manufacturing (22)

RM7 AltSynQXc ifo Business Climate Index Manufacturing (00)


RM8 AltSynQXs RWIISL-Container Throughput Index (00)

The first reference model RM1 only considers the time interdependency of the IPI for the nowcast

Here the autoregressive process of order one is applied (AR1)

RM2 includes the regressor BC the Business Climate Index for manufacturing For this model the

automated selection of ARMA parameters displays an autoregressive process of order one In

Germany the ifo Business Climate Index is widely used for economic nowcasts Apart from the

Truck-Toll-Mileage Index the ifo Business Climate Index is available relatively early and thus

represents an alternative to the Truck-Toll-Mileage Index as a regressor in nowcasting estimates

However the nowcast model RM2 with the concurrent regressor BC can only be conducted about

10 days after the model f2 from chapter 3 due to the longer timelag of the ifo Business Climate

Index

Another alternative is the RWIISL-Container Throughput Index (CT) which was developed by the

RWI and the Institute of Shipping Economics and Logistics (ISL) which aims at providing timely

information on short term trends in international trade The index is based on the consideration

that containers have become the most important means of transporting international products

Since German production is strongly interwoven internationally on both the input and the output

side it can be assumed that the RWIISL-Container Throughput Index is a good indicator of

production development The CT is issued with a timelag of about 25 days to the reporting

month Both the ifo Business Climate Index and the CT are hence considered for concurrent time

periods in RM2 and RM3 and for lagged time periods in RM4 and RM5

RM6 includes the lagged New Orders Index The reference model only considers the lagged

regressor because the nowcast of the concurrent regressor could be conducted only two days

before the target variable the Industrial Production Index is available

28

RM7 and RM8 are reference models for quarterly periodicity The timelag of quarterly statistics is

in general significantly higher than those of monthly statistics Therefore it is assumed that the

nowcast can be conducted at the point in time when the information from the ifo and RWI

institute are available Hence the difference to monthly nowcast models is that all regressors are

applied concurrently

The quality measures Q1 to Q8 for the reference models are in Table 7 compared to those of the

models f2 and f10 from chapter 3 This comparison is illustrated in figure 12 with a cobweb

diagram of the standardized nowcast results as in figure 10

Table 7 Quality measures for nowcast results of reference models

f2 RM1 RM2 RM3 RM4 RM5 RM6 f10 RM7 RM8

Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

For the monthly models it appears that model f2 is almost consistently better in terms of

precision than all reference models Regarding the bias however all monthly reference models

show better results than f2 For the quarterly models the results of the Truck-Toll-Mileage Index

cannot convince model RM7 consisting of the ifo Business Climate Index is in all quality

indicators at least as good as the results of f10

To include also the other models from chapter 3 the nowcast errors of f1 to f10 can be compared

to a reference model One approach that accomplishes this is the Theilrsquos inequality measure

(TIM) It sets the root mean squared error (RMSE) of the analysed forecast in relation to the one of

a reference model Since the numerator of U the nowcast error of the analysed nowcast should

be smaller than the reference nowcast Theilrsquos inequality measure should be smaller than unity

For the computation of Theilrsquos inequality measure for the models f1 to f9 the reference model

RM2 (concurrent ifo Business Climate Index) is chosen because it yields the smallest RMSE of all

reference models and is hence the most competitive model For f10 with quarterly data the

respective quarterly concurrent ifo Business Climate Index (RM7) was chosen

Table 8 Quality indicator for comparison of models

Q10 TIM Theilrsquos inequality measure TIM = radic

1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model

At least less than one

Table 9 shows the Theilrsquos inequality measure for the models f1 to f10

Table 9 Nowcast results for Q10

f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112

Regarding monthly data the reference model (RegARMA with ifo Business Climate Index) cannot

beat any of the monthly models f1 to f9 all including the Truck-Toll-Mileage Index For the models

with quarterly periodicity the reference model yields better results

6 Conclusion

The project tested ten relatively simple RegARMA models Elaborated estimation methods such

as vector autoregressive models could not be tested due to time constraints Likewise the

formation of regressors had to forego the integration of further information for example on the

traffic structure The results however give first indications of the usefulness of the Truck-Toll-

Mileage Index in producing a nowcast for the Industrial Production Index

For the conclusion one has to distinguish between two questions First of all the question arises

as to when a nowcast is in general good enough to be published The second question is whether

the Truck-Toll-Mileage Index is a superior basis for nowcasting Regarding the latter question the

results of the simple mechanistic nowcast calculations applied in this project have shown that

for monthly periodicity other indicators do not lead to better results The fact that the Truck-Toll-

Mileage Index is issued prior to the other here discussed indicators makes the index a superior

basis for mechanistic nowcast estimates However a severe problem with biasedness of the

results based on the Truck-Toll-Mileage Index occurred which should be addressed in further

30

research The application of further control variables accounting for structural changes in

transport services may be a way to address the issue

The second question is whether the quality is sufficient enough to publish nowcasts in the






7 Implementation plan

The project results serve as input in order to be able to discuss and develop the nowcast for the

Industrial Production Index in a second development phase in cooperation with possibly

interested actors from the field of economic analysis in Germany (for example Deutsche

Bundesbank Federal Ministry of Economic Affairs German Council of Economic Experts

commercial banks) After completion of the second development phase the results of the

nowcast could be published in EXDAT which is the German publication format for experimental

statistics in official statistics Contents of EXDAT are data from new digital data sources from

newly developed methods or innovative IT developments as well as general information on

innovative methodological approaches within official statistics The core element is a feedback

option for users which will be used to further develop the experimental approaches The goal of

publishing results in EXDAT is ultimately a later publication as official statistics The descriptions

of the development steps undertaken in this project on nowcasting the Industrial Production

Index will be published in EXDAT

Annex

In the following Annex A B C and D are presented Annex A contains the English translation of a

joint essay published in German language by the Federal Statistical Office in December 2018 in

cooperation with the German Federal Office for Goods Transport Annex B shows the graphical

explorative analyses of the interrelation of the Truck-Toll-Mileage Index and further short-term

statistics Annex C shows tables containing the Industrial Production Index the applied

regressors nowcasts and nowcasts errors Finally Annex D contains the R code that was used to

do the nowcast calculations

Statistisches Bundesamt (Federal Statistical Office) | German version published in WISTA | 6 | 2018 p 11 et seq 1

Keywords digitalisation ndash new digital data ndash truck-toll-mileage index ndashshort-term economic indicator ndash industrial production

ABSTRACT

Economic activity generates and requires transport services ndash hence there is a close connection between the economic development and the freight traffic by trucks As part of toll collection digital process data are generated among other things on the mileage of trucks subject to toll The Federal Office for Goods Transport has used these data to develop a truck-toll-mileage index which indicates the change in mileage for comparable basic variables and excludes structural changes as far as possible Due to its early availability and economic meaningfulness the Federal Statistical Office has included this index in its publication programme This article describes the new element of official short-term economic statistics and explains its relation to existing short-term statistics

ANNEX A -DIGITAL PROCESS DATA FROM TRUCK TOLL COLLECTION AS NEW BUILDING BLOCK OF OFFICIAL SHORT-TERM STATISTICS

Michael Cox Martin Berghausen Dr Stefan Linz Dr Claudia Fries Julia Voumllker

Michael Cox and Martin Berghausen

are working at the Federal Office

for Goods Transport Michael Cox is

in the section ldquoMarket monitoringrdquo

responsible for traffic analyses ndash

based on the truck toll data and

other traffic data

Martin Berghausen is in the section

ldquoAir Transport Related Affairs Sta-

tisticsrdquo engaged in the monitoring

and analyses of the international

aviation markets

Dr Stefan Linz Dr Claudia Fries and Julia Voumllker

are working in the Federal Statistical

Office in the section ldquoShort-term

Economic Indices for Industry

Methods Development for Short-

term Statistics Seasonal Adjust-

mentrdquo


1

The truck-toll-mileage index

11 Truck toll data

A distance-based toll was introduced for heavy goods vehicles (trucks) in Germany at the beginning of 2005 The toll obligation initially applied to trucks with a gross vehicle weight rating (GVWR) of 12 tonnes and above on the approximately 12800 km of federal motorways In the ensuing years the toll obligation was successively extended and now applies to all trucks of 75 tonnes GVWR and above on all federal motorways and federal roads The amount payable depends on the distance travelled on the tollable roads the number of axles of a vehicle or vehicle combination and its emission class The Federal Office for Goods Transport performs the sov-ereign tasks regarding the implementation of the truck toll A private operator Toll Collect GmbH was entrusted as officially appointed agent with setting up and operat-ing the toll collection system

Users of the tollable road network have to log in to the toll system for the settlement of truck tolls Various options are available Users can log in automatically via the vehicle device or they can do so manually at toll terminals or via the Internet The automatic log-in sys-tem is based on a combination of mobile phone (GSM) and satellite positioning (GPS) technologies Automatic log-in uses a vehicle device the so-called On-Board Unit (OBU) It uses satellite signals to determine the position

of and distance covered by the vehicle and transmits the data to the operatorlsquos computer systems via mobile communication This log-in method was used for around 96 of all tollable journeys in 2017 Figure 1

When truck toll collection was introduced the Federal Office for Goods Transport set up an information system which allowed central evaluation of all the key figures required for controlling and monitoring the operator These include data on truck toll receipts tollable jour-neys and mileage These truck toll data are collected by the truck toll operator and forwarded to the Federal Office for Goods Transport They can be broken down by various criteria ndash for example country of origin of the truck number of axles emission class or log-in source Truck toll data have been processed since 2008 in the form of administrative statistics and published monthly and annually on the Federal Office for Goods Transport website | 1 The monthly toll statistics reports are usually published 15 working days after the end of the refer-ence month and contain numerous evaluations of truck toll data that can be used for traffic management stud-ies For example tollable journeys and mileages are dis-played graphically and in tabular form differentiated by country of registration axle class and emission class In addition the monthly toll revenues for each tollable section of road are made available on the websites of the Federal Office for Goods Transport or the mCLOUD research platform in accordance with Section 9 (7) of the German Federal Trunk Road Toll Act differentiated by emission class and axle class | 2

Excursus

mCLOUD is a research platform containing open dataon mobility and related topics In mCLOUD the FederalMinistry of Transport and Digital Infrastructure providescentral access to all its open data (and those of its subor-dinate authorities) and also allows private mobility sec-tor providers to offer their data there

The tollable truck traffic recorded in the truck toll data is almost identical to the actual truck traffic of 75 tonnes GVWR and above on the German trunk roads and thus

1 See Federal Office for Goods Transport [Accessed on 25 October 2018] Available at wwwbagbundde

2 Act on the Levying of Distance-Related Charges for the Use of Federal Motorways and Federal Highways (Federal Trunk Road Toll Act ndash BFStrMG) of 12 July 2011 (Federal Law Gazette I p 1378) last amended by Section 21 of the Act of 14 August 2017 (Federal Law Gazette I p 3122)

Figure 1On-Board Unit for satellite-based vehicle log-in to the truck toll system

With permission of Toll Collect GmbH2019 - 01 - 0090

Statistisches Bundesamt (Federal Statistical Office) | German version published in WISTA | 6 | 2018 p 11 et seq2

Digital process data from truck toll collection as new building block of official short-term statistics

represents a quasi complete count in this area There are only a few exceptions to the toll obligation (eg armed forces and police vehicles) and the proportion of violations of the truck toll obligation is assessed as very low Since most of the data are satellite-generated process data there is low susceptibility to revision At the same time the truck toll data are available in unpro-cessed form within a few days of the end of each refer-ence month Short delays only occur by the OBU sending collected sets of information to the operator who then forwards them to the Federal Office for Goods Transport with a time lag | 3 Finally anonymisation and processing of the data also require a certain amount of time

However the fact that the truck toll has been gradually extended since its introduction is relevant depending on the intended use of the data | 4 Overview 1

Figure 2 shows the development of toll road mileages since 2005 The vertical lines mark the dates of the toll extensions Accordingly the reduction in the tonnage limit for the determination of the truck toll from the end of 2015 and the extension of the toll obligation to include all federal roads from mid-2018 led to signifi-cant increases in the tollable truck mileage

3 At present the OBU transmits data whenever the engine of the truck is started and then subsequently every four hours in Germany and once a day abroad When the engine is off no information collected since the last transmission is sent until the engine is restarted

4 The individual tollable road sections can be viewed See Federal Highway Research Institute [Accessed on 25 October 2018] Avail-able at wwwMauttabellede Section 1 of the Federal Trunk Road Toll Act states that individual sections of the A5 and A6 federal motor-ways on the German-French and German-Swiss borders are perma-nently exempt from tolls

Overview 1Introduction and extensions of toll obligation

Introduction or extension of toll obligation

01012005 Truck toll obligation for 12t GVWR and above on all federal motorways (12800 km)

01012007 Toll extension + 42 km of federal roads to tollable road network



01102015 Reduction of the tonnage limit to vehicles of 75 t GVWR and above

01072018 Toll extension + all federal roads (38000 km) to the tollable road network

A total of 53000 kilometres of road are now tollableSource Federal Office for Goods Transport



12 The truck-toll-mileage index

The time series of the toll statistics accurately reflects the development of the truck toll mileage Any attempts to relate this to the development of goods transport or the short-term economic development are however undermined by the fact that the toll extensions restrict any comparability of the truck toll data over time The Federal Office for Goods Transport developed the ldquoTruck-toll-mileage indexrdquo in order to exclude changes in the observed mileage from the time series that are caused by toll obligation extensions This index represents the development of mileage as a fixed base index for a subpopulation that can be presented in unchanged form over time | 5 First the truck-toll-mileage index only includes the mileage of trucks on federal motorways as the road section-based extensions of the toll obliga-tion were always related to federal roads Second only mileages of trucks with at least four axles are included in the fixed base index since in most cases these are

5 In a ldquofixed base indexrdquo the observation units refer to a part of the population which is delimited equally over the whole index period

not affected by the toll extensions to include trucks with a GVWR of up to 12 tonnes | 6 Since the last extension of the toll obligation in July 2018 the truck-toll-mileage index has included an average of around 72 of all toll mileages Up to autumn 2015 this share was between 90 and 95 Only with the larger truck toll extensions the lowering of the tonnage limit and the extension to include all federal roads did the share decrease signifi-cantly

Figure 3 shows the development of the truck-toll-mile-age index in comparison with the total tollable mileage presented above For simplification both time series were standardised to their average 2005 valuesThe dif-ference between the truck-toll-mileage index and the total tollable mileage initially rises only slightly following the first extensions to the toll obligation with significant differences only becoming apparent from autumn 2015

In addition to representing the truck-toll-mileage index as a fixed base index it is also possible to compute a

6 Direct breakdown by GVWR is currently not possible with the truck toll data

Toll extension +42 km federal roadsToll extension +1100 kmfederal roads

Toll extension 75 t GVWRand above

Toll extension allfederal roads

Introduction oftoll obligation

Source Federal Office for Goods Transport

Figure 2Total monthly tollable mileage and toll extensionsBillions of km

2019 - 01 - 0091

0

1

2

3

4

J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J O J A J2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018



chain index | 7 Here the index includes the total mileage of trucks tollable at a given time on all roads tollable at this time Informed estimates of the month-on-month rate are made for the months with toll extensions For example the mileage for the month of July 2018 is esti-mated for the tollable roads on the basis of the June 2018 status The estimated values of hypothetical unchanged road networks and tonnage limits are used for back cal-culations based on month-on-month change rates A comparison between the truck-toll-mileage index as a fixed base index and as a chain index revealed only very small differences between the two time series Since the fixed base index is easier to interpret and above all because it can be calculated automatically even in the event of toll extensions the truck-toll-mileage index is published as a fixed base index

Automation of the calculation and data provisioning pro-cesses is important because the mileage index is issued within a few days of the end of each reference month

7 In the case of a ldquochain indexrdquo the delimitation of the relevant obser-vation units may change during the life of the index

Analyses by the Federal Office for Goods Transport have shown that the toll data are almost complete ten days into the following month After that the further toll data added to the overall database amounts to less than one per cent of the total This is the case for example if the On-Board Unit in a truck is switched off for several weeks and the remaining data are not forwarded to the toll sys-tem until it is switched on again

The truck-toll-mileage index of the Federal Office for Goods Transport provides an indicator with early avail-ability Much of the goods traffic on trunk roads can be assigned to the motorways meaning that the truck-toll-mileage index provides a good indicator of total road freight transport | 8 In the future it could be useful to publish a further index which reflects the road freight transport on federal roads At present however only a short time series of truck toll data is available for the

8 Approximately 80 of tollable mileage is driven on federal motor-ways with federal roads only accounting for about 20 No informa-tion is available on the truck mileage on the secondary road network (Land roads district roads and municipal roads)






Figure 3Truck-toll-mileage index and total mileage of all tollable vehicles2005 = 100

2019 - 01 - 0092

80

100

120

140

160

180


Truck-toll-mileage index Total mileage of all tollable vehicles



entire federal road network (from July 2018) | 9 Road freight transport accounts for a large proportion of the total transport performance in all transportmodes It thus represents a large proportion of all domestic freight transport (BMVI 2017 p 242)

2

Relationship between mileage and industrial production

21 Short-term statistics of the Federal Statistical Office

The relationship between the truck-toll-mileage index and the results of short-term statistics from the Federal Statistical Office was examined based on the produc-tion index for manufacturing as part of the cooperation project between the Federal Office for Goods Transport and the Federal Statistical Office | 10 Short-term sta-tistics are used to measure among other things the economic activity of establishments and enterprises in Germany Statistical characteristics include the develop-ment of industrial production volume trade turnover or the results of quarterly domestic product calculations for the German economy as a whole

Short-term statistics often focus more on the develop-ment of results over time than on the absolute values of individual reference months or quarters Publications therefore give priority to rates of change referring to an earlier period for example the change in the production index in relation to the previous month which is referred to as month-on-month rate Month-on-month or quarter-on-quarter changes are often strongly influenced by sea-sonal effects which make it difficult to assess current developments The results of short-term statistics are therefore usually seasonally adjusted (including a cal-

9 A separate analysis of truck traffic on federal roads could provide additional information as it differs from traffic on federal motorways at the system level For example there is proportionately more regional and local traffic on federal roads than on federal motorways meaning that there are comparatively more German and small trucks on the federal roads

10 The project work corresponding to the Federal Statistical Office was carried out as part of the EU grant agreement number 822695-2018-DE-ESS-VIP-ADMIN

endar adjustment in most cases) Furthermore trends are calculated which indicate the medium-term devel-opmentof short-term statistics

The development of the non-seasonally adjusted pro-duction index for the manufacturing sector is compared below with the truck-toll-mileage index (also non-sea-sonally adjusted) This is followed by an analysis of the respective seasonally adjusted values and finally the trend developments

22 Statistical relationship in the rates of change of the unadjusted indices

Figure 4 shows the month-on-month rates of the non-seasonally adjusted production index for manufactur-ing on the Y-axis and the corresponding changes in the truck-toll-mileage index on the X-axis The Bravais-Pear-son correlation and a regression line for simple linear regression are given to provide orientation regarding the strength and direction of the statistical relationship | 11

11 The possibilities of more complex modelling of the relationship between mileage and production are discussed in Section 34

Figure 4Month-on-month rates of the non-seasonally adjusted indicespercent correlation coefficient (r) regression line

-30

-20

-10

0

10

20

30

Production indexManufacturing industry

-30 -20 -10 10 20 300Truck-toll-mileage index

r = 086

February 2005 to August 2018

Source Federal Statistical Office Federal Office for Goods Transport 2019 - 01 - 0093



The correlation coefficient of 086 indicates a clear statis-tical relationship between production and mileage Raw materials and intermediate products have to be trans-ported to the production sites and industrial products have to be delivered to the customers Freight services may therefore occur before during or after production In many areas of industry however delivery production

and transport are closely interwoven in just-in-time sup-ply chains The analysis of time series shifts has shown that the relationship is strongest between the produc-tion index and the mileage index for the same period ndash the statistical relationship is significantly weaker when comparing the production index with the mileage in pre-vious or subsequent months

Figure 5Month-on-month rates of the non-seasonally adjusted indices percent correlation coefficient (r) regression line

-30

-20

-10

0

10

20

30


-30 -20 -10 0 10 20 30Truck-toll-mileage index

r = 097

February 2005 to August 2018 ndash In brackets Share of the main industrial grouping in value added in manufacturing


-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080

Intermediate goods (37) Capital goods (46)

Consumer non-durables (14) Consumer durables (3)



The production index is calculated as a weighted average of the indices for individual economic activities | 12 The weighting reflects the share of the total value added that was achieved in the individual economic activities in the base year 2015 Figure 5 shows the production index for different sub-sectors The weight of the subdivisions in the total manufacturing index is indicated in brackets in each case They are classified into the main industrial groupings that is sub-aggregates of economic activi-ties | 13 The diagrams in figure 5 always show the same truck-toll-mileage index no differentiation by type of goods or economic activity is possible here

One such main industrial grouping is intermediate goods for example the production of basic chemicals or fabricated metal products The relationship between production and mileage is particularly clear here with a correlation coefficient of 097 for the non-seasonally adjusted month-on-month rates

Another main industrial grouping is capital goods for example the manufacture of machinery or vehicles Capital goods play a major role in the production index for the manufacturing sector accounting for almost 50 percent of value added The correlation coefficient here is only 063 The production of these goods can take a long time and some finished products such as ships aircraft or trains are not transported by road On the output side a looser relationship between production and mileage can be assumed for the production of capi-tal goods but substantial transportation is likely to be needed for the procurement of raw materials and inter-mediate products as input for production

The main industrial groupings also distinguish between consumer durables and consumer non-durables Con-sumer non-durables include pharmaceutical products foodstuffs or even clothing a very close relationship is apparent here too from the correlation coefficient of 091 For consumer durables such as furniture or household appliances the relationship between mile-age and production may be distorted to some extent by production in stock which is included in the produc-tion index ndash yet the correlation coefficient of 080 is still relatively high

12 For calculation of the production index see Linz et al 2018a

13 For the main groupings see Commission Regulation (EC) No 5862001 of 26 March 2001 implementing Council Regulation (EC) No 116598 concerning short-term statistics Definition of Main Industrial Groupings (MIGS)

In summary with regard to the month-on-month rates of the non-seasonally adjusted data a strong statisti-cal relationship can be observed between production in manufacturing and mileage this varies in strength between the various sectors Similar results can be seen when comparing the month-on-month rates of the turn-over index or the new orders index for manufacturing with the development of mileage although the relation-ship is somewhat weaker than in the production index A clear statistical relationship between mileage and economic activity could also be observed for domestic trade sectors (such as wholesale trade motor vehicle trade) the latter being measured by turnover Further-more a clear statistical relationship can be measured between the quarterly mileage and the quarterly rates of change of the gross domestic product from the national accounts Finally as expected the quarterly turnover in the ldquoFreight transport by road and removal servicesrdquo sector also correlates with the mileage

23 Statistical relationship in the rates of change of the seasonally adjusted indices

As mentioned above the developments in short-term statistics are often strongly influenced by seasonal effects which is why short-term statistics are usually seasonally adjusted | 14 This is based on the assump-tion that a time series can be divided into a number of components The seasonal component includes annu-ally movements recurring in the same months in similar intensity The calendar component contains the average influence of the calendar constellations that result for example from the shift in the number of working days in months of the same name The trend-cycle component tracks short-term fluctuations and long-term develop-ment trends The irregular component comprises both random and economically explicable influences which have a short-term effect and which do not belong to the other components ndash such as the effects of strikes on production within an industry In seasonal adjustment the seasonal and calendar components are excluded from the results since the expected fluctuations of

14 The term seasonal adjustment is used in this paper as a collec-tive term for the adjustment of seasonal fluctuations and calendar effects Regarding seasonal adjustment by the Federal Statistical Office see Linz et al 2018b



these components can obscure the relevant move-ments in a time series

The seasonal adjustment method X13 in JDemetra+ which is used by the Federal Statistical Office to cal-culate the seasonally adjusted data of the production index was also applied to the truck-toll-mileage index Figure 6 shows as an example the seasonal com-ponent of the production index for intermediate goods together with the seasonal component of the truck-toll-mileage index | 15 The seasonal components are shown for the months January 2011 to December 2017 There is strong accordance between the course of both seasonal components The decline in December and the subse-quent spring revival are somewhat stronger in the pro-duction index for intermediate goods than in the mileage index In June the mileage is regularly slightly below and in autumn above the production of intermediate goods Otherwise both seasonal patterns are almost identical The specification parameters of the production index for intermediate goods were adopted for the seasonal adjustment of the truck-toll-mileage index in figures 7

15 Seasonal adjustment of the production index for manufacturing is carried out at the breakdown level of the main industrial groupings in the X13 method in JDemetra+ Intermediate goods are presented here as an example because the strongest statistical relationship with mileage can be observed for this main industrial grouping

and 8 | 16 There is also very strong accordance between the seasonal patterns if the specification parameters for controlling the seasonal adjustment for the production index and the mileage index are determined indepen-dently of each other

Figure 7 shows the month-on-month rates of the four time series components in scatterplots The correlation coefficient is 098 for the month-on-month rate of the seasonal component The statistical relationship in the calendar component is even stronger differences in the working days of the individual months have a very simi-lar effect on production and mileage | 17

The calendar and seasonal components are removed from the time series in the adjustment meaning that

16 The X13 method in JDemetra+ offers various possibilities for taking the specific conditions of a time series into account when determin-ing the time series components In order to use these various speci-fication parameters must be defined with regard to the RegARIMA model being used the trend and seasonal filters and other options

17 Since the same seasonal adjustment method was chosen for the truck-toll-mileage index and the relevant production index the same calendar regressors are used as explanatory variables in the RegARIMA model However the coefficients of the regressors are estimated separately for each time series Accordingly the number of working days in a month may have a different influence on the mile-age than on the production of goods For a description of the calen-dar adjustment in the intermediate goods production index see Linz et al 2018b

Source Federal Statistical Office Federal Office for Goods Transport

Figure 6Seasonal components

2019 - 01 - 0095

08

09

10

11

J A J O J A J O J A J O J A J O J A J O J A J O J A J O2011 2012 2013 2014 2015 2016 2017

Truck-toll-mileage index Production index for intermediate goods



both the trend-cycle component and the irregular com-ponent are included in the calendar and seasonally adjusted result The lower part of Figure 7 compares the month-on-month rates of the production index for inter-mediate goods and the truck-toll-mileage index for these two components Looking at the trend the accordance between the development of mileage and production is weaker than for the seasonal component however the correlation is relatively high here at 085 The variance caused by trend movements is weak compared to the seasonal variations especially after the strong move-ments caused by the economic financial and euro cri-

ses There is no significant change in the strength of the statistical relationship between the trend developments in mileage and intermediate goods production if only the period from 2012 is considered The cyclical char-acteristics of the time series for mileage and production are discussed in more detail in the following section

The scatterplot of the month-on-month rates for the irregular component shows that the irregular movements of the truck-toll-mileage index may differ significantly from those of the intermediate goods production index Irregular fluctuations are relatively weakly correlated In

Figure 7Month-on-month rates for the time series components of the indices percent correlation coefficient (r) regression line

-20

-10

0

10

20

Production indexfor intermediate goods

-20 -10 0 10 20Truck-toll-mileage index

r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036

Seasonal component Calender component

Trend-cycle component Irregular component



production for example irregular movements can occur due to technical disruptions in the production processes in the establishments or due to unusual holiday constel-lations In the case of truck mileage traffic restrictions on larger stretches of road due to major roadworks or snow and icy roads can lead to irregular movements for example Official statistics provide little information on the relevance frequency and impact of such events this information cannot be gathered because of the burden on respondents | 18 Some of the influencing variables such as lengthy strikes could affect both production and mileage In many cases however there are presum-ably different causes of irregular fluctuations in produc-tion and mileage or common causes of fluctuations are reflected differently in production and mileage

As mentioned above the seasonally adjusted result includes both the irregular component and the trend-cycle component In a retrospective analysis the trend-cycle component is very well suited for identifying economic turning points In practice however it is hardly used in the analysis of current economic developments Due to its calculation method the trend reflects changes but with a time lag and deviations from the previous trend (assuming a constant calendar and seasonal pattern) are initially included in the irregular component Only if the new tendency is confirmed by further data points will it be reflected by the trend For analysing the most recent economic developments the use of seasonally adjusted results has therefore become common practice (see for example Deutsche Bundesbank 1999 p 41 ff)

Conversely the strong accordance between the season al mileage and goods production patterns as shown above means that the use of seasonal adjustment excludes a significant degree of covariance between the two vari-ables from the data Figure 8 shows the month-on-month rates of the calendar and seasonally adjusted indices in a scatterplot Here the development of the production index for the manufacturing sector as a whole is once again shown on the Y-axis and the cor-

18 The ifo Institute for Economic Research also gathers rough appraisals of the relevance of certain impediments to production in its manager surveys Managers are asked for example whether production in their own company has been hindered in the current month through a lack of raw or intermediate materials or through insufficient tech-nical capacity However such questions are only included in the questionnaire on a quarterly basis A comparison with the irregular components of the production index for intermediate goods and the truck mileage index aggregated to quarterly results reveals little accordance

responding changes in the truck-toll-mileage index are plotted on the X-axis The correlation coefficient is 054 which is significantly lower than in the non-seasonally adjusted time series In the production index for manu-facturing it is still relatively high while the other main industrial groupings or aggregates yield a somewhat lower correlation

24 Common path in economic cycle

Cyclical economic movements can be presented for example as deviations of a medium-term trend from the long-term growth path of a time series The Federal Statistical Office uses the BV41 method to calculate medium-term trends (Speth 2004) It is particularly suit-able for mapping economic movements that span three or more years At the same time it smoothes out intra-annual fluctuations to a considerable extent Cyclical economic movements can be presented in isolation by examining the deviation of a BV41 trend from its long-term growth path Figure 9 shows the cyclical devel-opments of the production index for manufacturing and the truck-toll-mileage index | 19

19 The long-term growth component was calculated by applying a Hodrick-Prescott filter (HP trend) with smoothing parameter λ = 1 mil-lion For HP trend see HodrickPrescott1997

Figure 8Month-on-month rates of the seasonally adjusted indicespercent correlation coefficient (r) regression line

-20

-10

0

10

20



r = 054


Source Federal Statistical Office Federal Office for Goods Transport2019 - 01 - 0097



A review of the entire time series reveals several exam-ples of precise accordance between the economic turn-ing points at other points however there are divergent developments For both time series the downward movement caused by the economic and financial crisis begins simultaneously in February 2008 with both dips coinciding in July 2009 The decline caused by the euro crisis appears two months earlier in the truck-toll-mileage index than in the production index for the manufacturing sector ndash while the dip occurs at exactly the same time in both time series the peaks of the subsequent recovery also coincide In the years 2015 and 2016 the devel-opment of the mileage index seems to have decoupled itself from the development of production this period is characterised by less pronounced cyclical movements in the production index A common turning point can be observed again at the turn of 20172018 This appears in the mileage index only one month earlier

When interpreting the common economic cycle it should be noted that mileage and production are very different

variables The truck mileage indicates the total distance travelled it contains no information on the value and is only indirectly related to the quantity of goods trans-ported The production index on the other hand also refers to monetary variables and its purpose is to show the development of the total value of goods produced at constant prices | 20 The statistical relationship between mileage and production which is nevertheless clearly discernible can be influenced by structural changes in industrial demand for freight services For example it is noted that increasing volumes of higher-value goods are being transported an increasing proportion of which by road and involving longer transport distances The trans-port of bulk goods by contrast is declining (SSP Con-sult 2018 pp 31 and 38) Higher-value goods are to be found for example in the main industrial grouping of consumer durables Figure 10 shows the production

20 Since the weighting of the production index is based on the sum of gross value added in the economic activities it can also be regarded as a calculation system for the monthly rolling forward of gross value added at constant prices (see Strohm 1985 here page 23)


Figure 9Economic cycle as deviation of medium-term trend from long-term trend Truck-toll-mileage index and production index for themanufacturing industry

2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14

Truck-toll-mileage index Production index Manufacturing industry

Mar 13

Jul 09

Feb 08



index cycle for consumer durables alongside the devel-opment of the truck-toll-mileage index

There was a joint upward tendency in 2015 and 2016 with both the production of consumer durables and road freight transport increasing during this period The increase in this industrial production sector is scarcely reflected in the production index for total manufactur-ing as this main industrial grouping only accounts for roughly 3 of the total index Other possible factors that could have influenced the growth in mileage dur-ing this particular period include the increasing sales of German industrial companies to euro area countries and low fuel prices | 21

The total freight mileage required by industry can also be provided by different combinations of transport

21 For example the industrial turnover index shows that sales posted by German industrial companies to the euro area countries have risen significantly since around 2014 but this is not reflected in the production index the destination of the goods produced is not taken into account in the calculation of the production index

types ndash the share of road freight transport may change within the total domestic freight transport volume Trans-port statistics show however that the modal split (dis-tribution of transport volumes across different means of transport) is relatively stable in the long term on the basis of annual averages One of the reasons for this is that individual branches of industry have an affinity for certain modes of transport

The link between mileage and production may also be affected by shifts within road freight transport for exam-ple by an evasive response to the extension of the toll obligation to federal roads Studies show that there have only been isolated instances of traffic evasion on certain sections of road Toll evasion evidently yields little or no cost advantage for the transport companies in most cases and it can lead to lost time for the companies (Deutscher Bundestag 2016) Nor is there any particu-larly pronounced trend towards the use of trucks below the limit of 75 t GVWR Structural changes in road freight traffic as measured by the truck-toll-mileage index may

-015

-010

-005

005

010

015

0


Nov 17


Truck-toll-mileage index Production index Consumer durables

Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09

Figure 10Economic cycle as deviation of medium-term trend from long-term trend Truck-toll-mileage index and production index forconsumer durables



also occur if for example the mileage share of trucks with at least four axles on journeys with no freight (empty runs) increases on federal motorways These cannot be distinguished in the toll data from freight transport jour-neys Furthermore from the truck toll data it is not possi-ble to identify transit journeys in which the German road network is used by trucks containing goods which are neither loaded nor unloaded in Germany Here too the proportion of transit journeys can change over time It is beyond the scope of this paper to discuss the effect of such factors in more detail

When using toll data for economic monitoring pur-poses it should always be borne in mind that truck mileage can only provide a rough basis for assessing the development of economic activity in Germany Too much importance should therefore not be attached to the latter factors

25 Conclusions regarding the relation-ship between mileage and industrial production

The non-seasonally adjusted values show a clear statis-tical relationship between the production and truck-toll-mileage indices Much of this is probably attributable to common seasonal movements Regular intra-annual fluctuations in production may impact on truck mileage as the result of production company demand for freight transport in some cases factors such as typical annual weather fluctuations may have a similar effect on pro-duction and freight traffic

The strong similarities in both the seasonal pattern and the calendar effect imply that applying seasonal adjust-ment methods excludes some of the covariance from the data The irregular movements as part of the season-ally adjusted time series reveal scarcely any accordance between production and mileage development In road freight transport and the production of intermediate goods there would appear to be few common causes of the exceptional short-term influences or they have very different effects on the two variables Seasonally adjusted results which play an important role in the analysis of recent economic developments also show a correlation between mileage and industrial produc-tion However this is significantly lower than in the non-adjusted figures

As mentioned above the trend-cycle component is very well suited for the retrospective identification of economic turning points despite its rarely being used for current economic development The cyclical course of economic activity measured by the deviation of medium-term developments from the long-term trend reveals a number of common developments particularly at the economically relevant turning points The peaks and dips during the economic financial and euro cri-ses are often shown in exactly the same month and in some cases the economic turning points are only a few months apart In 2015 and 2016 the two indices fol-lowed different trends In this phase structural changes in industrial activity may be relevant which are reflected in the truck-toll-mileage index but not in the production index for manufacturing Such structural changes could form the subject of future investigations

Overall there is a clear statistical relationship between the truck-toll-mileage index and various short-term sta-tistics in particular the production index Since the truck-toll-mileage index is available roughly one month earlier than the production index it could make a useful contribution to the statistical description of short-term economic development in Germany

3

Truck-toll-mileage index as a new building block of official short-term statistics

31 Deployment of the truck-toll-mileage index in the short-term indicators

The truck-toll-mileage index is to be used as an addi-tional short-term indicator due to the characteristics outlined above and its early availability With the aim of offering the index at a place where data users regularly access short-term information the truck-toll-mileage index was included in the data offered by the Federal Statistical Office within the framework of a partnership between the Federal Office for Goods Transport and the Federal Statistical Office Existing official short-term sta-tistics such as the production index for the manufactur-



ing sector provide a much more accurate picture of eco-nomic developments broken down by economic activity (they are the results of surveys on production activi-ties or turnover in production trading or service enter-prises) However this information is not available until at least 35 days after the end of the reference month The truck-toll-mileage index provides a rough approxi-mation of production or trading activities and does not allow any breakdown by economic activity However its results are available within roughly ten days and thus much earlier than the established official statistics on economic activity

In the first project step the truck-toll-mileage index was made available in the ldquoShort-term indicatorsrdquo section on the website of the Federal Statistical Office This part of the Federal Statistical Officelsquos website provides an overview of time series from official statistics with eco-nomic relevance The data are displayed in figures and tables | 22 The truck-toll-mileage index is presented as a time series starting in reference month January 2005 Once the regular data delivery processes in the Fed-eral Office for Goods Transport and the data processing

22 Certain web browsers (eg the Windows Internet Explorer) can export the data from the tables and save them for example in MS Excel

processes in the Federal Statistical Office are properly established the results of every new month will regu-larly be included in the truck-toll-mileage index ndash and the index will be updated if necessary ndash from the begin-ning of 2019 This will take place on pre-determined dates approximately ten days after the end of each ref-erence month Figure 11

The delayed data transmissions from On-Board units to the toll system described in the first chapter may result in revisions of the truck-toll-mileage index in the month following the first publication but in most cases these only increase the levels by a small degree Experience has shown that in later months the number of automat-ically delivered time-lagged transmissions is so small as to render further adjustments to the index unneces-sary The truck-toll-mileage index is presented as a non-seasonally adjusted time series in seasonally adjusted form and as a BV41 trend Seasonally adjusted results and trend values may include additional revisions of previous results due to updates of seasonal and trend estimates The base year of the truck-toll-mileage index is determined by the publications of the Federal Statisti-cal Office concerning industrial short-term indices It is therefore initially set at 2015 and updated every 5 years

Figure 11ldquoShort-term Indicatorsrdquo screenshot from wwwdestatisde gt Facts amp Figures

2019 - 01 - 0100



Differentiations in the tollable mileage for example by country of registration or emission class are available in the existing publication programme of the Federal Office for Goods Transport (see Section 11) albeit with a longer time lag and without adjustment for structural changes in the toll collection through index calculation No subdivisions of the mileage index are therefore being offered in the publication programme of the Federal Sta-tistical Office in the first step of the cooperation project Similarly no variants of the truck-toll-mileage index such as an index for the number of tollable journeys are provided The time lag in the provision of the relevant short-term statistical information is to be kept as short as possible by reducing the toll data publication pro-gramme of the Federal Statistical Office

32 Deployment of the truck-toll-mileage index in the Business Cycle Monitor of the Federal Statistical Office

The truck-toll-mileage index has also been included in the so-called Business Cycle Monitor of the Federal Statistical Office The Business Cycle Monitor is an inter-active web application of the Federal Statistical Office the purpose of which is to illustrate the short-term eco-nomic development in Germany Here the medium-term cyclical movement of an indicator is compared with the level of the long-term trend of the same indicator An

indicator which increasingly exceeds its long-term trend for example heralds a boom phase The cyclical trend development is determined using the method described in Section 24 which is based on the deviation of the medium-term from the long-term development

The Business Cycle Monitor includes quadrant and dia-gram views The quadrant view shows the movement of the various indicators in a four-field system cover-ing the basic economic phases The indicators pass through the fields over time as dynamically moving data points In the diagram view the relationship between the medium-term and long-term trends is displayed as a static line chart In both views the desired time series can be clicked on to select it for display Figure 12

Comparing the medium-term movement of a time series with its own long-term trend the indicators are presented dimensionless in the Business Cycle Monitor This also allows indicators from different areas to be compared and contrasted the development of retail sales indus-trial production gross domestic product and the truck-toll-mileage index can be compared for example Here again the truck-toll-mileage index has the advantage of being available at a very early stage The addition of the mileage index means that the Business Cycle Monitor can provide an initial indication of the economic devel-opment in Germany within ten days or so of the end of a reference month In addition to the quadrant and dia-gram views the Business Cycle Monitor includes table

Figure 12Business Cycle Monitor of the Federal Statistical Office

Quadrant view Diagram view

wwwdestatisde gt Business Cycle MonitorSource Federal Statistical Office Federal Office for Goods Transport 2019 - 01 - 0101



views containing the underlying data These show the original values on which the presentation is based as well as medium-term and long-term trends

33 Other possible publication formats

The next project step involves offering the truck-toll-mileage index in GENESIS-Online the central publica-tion database of the Federal Statistical Office Here the results can be downloaded conveniently and in different formats for example in MS Excel CSV or HTML Regis-tered users can also retrieve the data automatically There is no charge for retrieving tables they can be adapted to individual requirements by selecting certain options The extent to which subdivisions and variants of the truck-toll-mileage index should be offered here is being examined and will have to be weighed up in vari-ous ways (such as breadth of range versus rapid avail-ability added benefit versus provision costs)

34 Use of toll data to shorten the time lags in short-term statistics

The statistical relationship between mileage and indus-trial production which is being observed gives rise to the question of whether the data from the truck toll collec-tion could be used to shorten the time lags in short-term statistics For example it can be investigated whether the truck-toll-mileage index as an explanatory variable in regression analyses would be suitable for calculating early estimates for the production index on an ongo-ing basis The results of such estimates are referred to below as ldquonowcastsrdquo to indicate that their purpose is not to make forecasts about economic developments | 23 Rather the toll data would be used as a basis for inves-tigating whether digital process data can be deployed to improve the timeliness of official statistics without increasing the burden on respondents

A nowcast would have to be based on the seasonally adjusted results since these are the main focus of the first publications of the Federal Statistical Office | 24

23 For definition of terms see Berg 2017 here p 120

24 The European Union recommends prioritising seasonally adjusted results in the press releases for the first publication of short-term sta-tistics (see Eurostat 2015 here page 46 ldquoSeasonally adjusted data are the most appropriate figures to be presented in press releasesrdquo)

Studies on the generation of nowcasts for the season-ally adjusted production development on the basis of truck toll data have been carried out for example by the Deutsche Bundesbank (2010) AskitasZimmermann (2013) and Doumlhrn (2011) The studies at that time were based on total mileage as the truck-toll-mileage index was not yet available However temporal comparability problems arising from toll extensions were still of little relevance at the time In the studies different estimation methods were tested using regressions and RegARMA modelling While AskitasZimmermann were optimistic about the potential of the toll data Doumlhrnrsquos first prelimi-nary results were rather sobering All authors referred to the short time series at that time which only allowed pre-liminary conclusions to be drawn but expressed great interest in the toll data

Similar studies are being carried out as part of the above-mentioned cooperation project between the Fed-eral Office for Goods Transport and the Federal Statisti-cal Office based on the longer time series of almost 13 years which is now available The newly introduced data adjusted for structural changes can be used for this pur-pose First results indicate that the explanatory force of the toll data has not improved significantly as a result of the longer time series which is now available The time series component of irregular movements has a strong influence on the seasonally adjusted results There is still little accordance between irregular fluctuations in mileage and production An analysis of cyclical trend movements however indicates that business cycle developments are certainly reflected in the development of mileage in some cases showing clear accordance in the economic turning points In the future the project must investigate how this information content could be used

In principle linking the mileage information with the results of the existing surveys seems meaningful | 25 Toll statistics will not be able to replace the existing official statistics surveys because the development of the truck mileage can only provide a rough approximation of the target variable the development of the production value at constant prices Nor can the mileage data be used to draw conclusions about the development in different economic branches however data on the development

25 For deliberations on the interlinking of digital process data with the results of official statistics see WiengartenZwick 2017



of production differenciated by branch belong to the scope of the industrial production index Also whether or not it is possible to generate meaningful estimates always depends on the strength of the actual economic relationship between truck mileage and industrial pro-duction ndash and also on its long-term stability

4

Conclusions and categorisation of the project

Toll data hold high information value The data were therefore published on the Federal Statistical Office website both as a non-seasonally adjusted index and in a seasonally adjusted form They were also displayed as a trend and are to be updated regularly from the begin-ning of 2019 on pre-determined dates approximately ten days after the end of each reference month The truck-toll-mileage index has also been included in the Business Cycle Monitor of the Federal Statistical Office

The question of whether digital process data can be used to increase the timeliness of official statistics with-out increasing the burden on respondents is currently being considered First analyses of the joint project of the Federal Office for Goods Transport and the Federal Statistical Office show that there is a strong correlation between the non-seasonally adjusted results of mileage and production A clear correlation can also be observed with domestic trade certain service sectors and the overall economy However for monitoring current eco-nomic developments the development of seasonally adjusted results is relevant The statistical relationship between mileage and economic activity is significantly weaker in seasonally adjusted results A review of the cyclical development of the economy since 2005 reveals some very clear examples of coincidence between the economic turning points however In the years 2015 and 2016 there is less coincidence structural changes are likely to play a role here

The Federal Office for Goods Transport is eager to make its transport findings available to researchers political and economic decision-makers and the interested pub-lic Inter-agency cooperation with the Federal Statistical Office enables the Federal Office for Goods Transport

to contribute its expertise in the field of transport data analysis and to make a contribution to official short-term statistics with the truck-toll-mileage index The Federal Statistical Office too is of the view that cooperation with partners from various fields must be intensified in order to develop new digital data for official statistics (ThielMeinke 2017) The provision of the truck-toll-mileage index in the Federal Statistical Officersquos short-term statis-tics service also as a seasonally adjusted time series represents a step in this direction



LITERATURE

Askitas NikolaosZimmermann Klaus F Nowcasting Business Cycles Using Toll Data In Journal of Forecasting Volume 32 Issue 4 July 2013 [Accessed on 19 February 2019] Available at httpsonlinelibrary wileycomdoipdf101002for1262

Berg Andreas Erhoumlhung der Aktualitaumlt von Indikatoren In WISTA Wirtschaft und Statistik Edition 52017 pages 120 ff

Bundesministerium fuumlr Verkehr und digitale Infrastruktur (BMVI) Verkehr in Zahlen 20172018 Hamburg 2017 [Accessed on 22 October 2018] Available at wwwbmvide

Deutsche Bundesbank Monatsbericht September 1999 Frankfurt 1999 [Accessed on 22 October 2018] Available at wwwbundesbankde

Deutsche Bundesbank Monatsbericht Mai 2010 Frankfurt 2010 Page 66 f [Accessed on 22 October 2018] Available at wwwbundesbankde

Deutscher Bundestag Drucksache 1810567 Bericht uumlber die Verkehrsverlagerung auf das nachgeordnete Straszligennetz in Folge der Einfuumlhrung der Lkw-Maut 2016 [Accessed on 22 October 2018] Available at httpdipbtbundestagdedoc btd181051810567pdf

Doumlhrn Roland Analysen und Berichte ndash Konjunkturindikatoren Die Mautstatistik Keine ldquoWunderwafferdquo fuumlr die Konjunkturanalyse Wirtschaftsdienst 2011 Pages 863 ff [Accessed on 22 October 2018]

Eurostat ESS Guidelines for Seasonal Adjustment 2015 [Accessed on 22 October 2018] Available at httpseceuropaeu

Hodrick Robert JPrescott Edward C Postwar U S Business Cycles An Empirical Investigation In Journal of Money Credit and Banking Volume 29 (1) February 1997 Pages 1 ff [Accessed on 22 October 2018] Available at httpswww0gsbcolumbiaedu

Linz StefanMoumlller Hans-RuumldigerMehlhorn Peter Umstellung der Konjunkturindizes im Produzierenden Gewerbe auf das Basisjahr 2015 (2018a) In WISTA Wirtschaft und Statistik Edition 22018 pages 49 ff

Linz StefanFries ClaudiaVoumllker Julia Saisonbereinigung der Konjunkturstatistiken mit X-12-ARIMA und mit X13 in JDemetra+ (2018b) In WISTA Wirtschaft und Statistik Edition 42018 pages 59 ff

Speth Hans-Theo Komponentenzerlegung und Saisonbereinigung oumlkonomischer Zeitreihen mit dem Verfahren BV41 In Statistisches Bundesamt (editor) Methodenberichte Issue 3 2004 [Accessed on 22 October 2018] Available at wwwdestatisde



LITERATURE

SSP Consult Gleitende Mittelfristprognose fuumlr den Guumlter- und Personenverkehr Mit-telfristprognose Winter 20172018 WaldkirchKoumlln February 2018 [Accessed on 22 October 2018] Available at httpassetsbmede

Strohm Wolfgang Zur Aussage der Indizes der Nettoproduktion fuumlr das Produzierende Gewerbe ndash Moumlglichkeiten und Grenzen In Wirtschaft und Statistik Edition 11985 pages 21 ff

Thiel GeorgMeinke Irina Gut aufgestellt fuumlr die Zukunft ndash ein Dank an Dieter Sarreither In WISTA Wirtschaft und Statistik Edition 52017 pages 9 ff

Wiengarten LaraZwick Markus Neue digitale Daten in der amtlichen Statistik In WISTA Wirtschaft und Statistik Edition 52017 pages 19 ff

LEGAL BASIS

Act on the Levying of Distance-Related Charges for the Use of Federal Motorways and Federal Highways (Federal Trunk Road Toll Act ndash BFStrMG) of 12 July 2011 (Federal Law Gazette I p 1378) last amended by Section 21 of the Act of 14 August 2017 (Federal Law Gazette I p 3122)

Commission Regulation (EC) No 5862001 of 26 March 2001 on implementing Council Regulation (EC) No 116598 concerning short-term statistics as regards the definition of Main Industrial Groupings (MIGS)


Extract from the journal WISTA Wirtschaft und Statistik

Published by Statistisches Bundesamt (Federal Statistical Office)

wwwdestatisde

You may contact us at wwwdestatisdekontakt

Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd

= Wirtschaft und Statistik

= annual average

= average (for values which cannot be added up)

= quarter of a year

= half-year

= not elsewhere classified

= no main economic activity

= piece

= million

= billion

Explanation of symbols

ndash = no figures or magnitude zero

0 = less than half of 1 in the last digit occupied but more than zero

= numerical value unknown or not to be disclosed

= data will be available later

X = cell blocked for logical reasons

I or mdash = fundamental change within a series affect-ing comparisons over time

= no data because the numerical value is not sufficiently reliable

( ) = limited informational value because numerical value is of limited statistical reliability

copy Statistisches Bundesamt 2019 Figures have in general been roundes without taking account of the totals so that there may be an apparent slight dis-

Reproduction and distribution also of parts are permitted crepancy between the sum of the constituent items and the provides that the source is mentioned total as shown

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in industry Mrz 19

Seasonally adjusted

Absolute values Period-to-period change Absolute values Period-to-period change

Cross-correlation

Cross-correlation period-to -period change

TrendsEconomic cycle Hodrick-Prescott trend

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacturing Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in intermediate goods Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in capital goods Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in durable goods Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in non-durable goods Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in mining and quarrying Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in construction of buildings Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in civil engineering Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in building completion work Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Feb 19

Seasonally adjusted


Cross-correlation



Index of turnover in wholesale and comission trade without motor vehicle

trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of turnover in retail trade Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of turnover in motor vehicle trade Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in mining and quarrying sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in manufacturing sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in intermediate goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in capital goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in durable goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in non-durable goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in mining and quarrying sales direction domestic territory Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in manufacturing sales direction domestic territory Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in intermediate goods sales direction domestic territory Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in capital goods sales direction domestic territory Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in durable goods sales direction domestic territory Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in non-durable goods sales direction domestic territory Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in mining and quarrying sales direction euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in manufacturing sales direction euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in intermediate goods sales direction euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in capital goods sales direction euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in durable goods sales direction euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in non-durable goods sales direction euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in mining and quarrying sales direction non-euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in manufacturing sales direction non-euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in intermediate goods sales direction non-euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in capital goods sales direction non-euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in durable goods sales direction non-euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of turnover in non-durable goods sales direction non-euro area Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of new orders in manufacturing sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of new orders in intermediate goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of new orders in capital goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of new orders in durable goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41

(x-axis) Truck-toll-mileage index Apr 19(y-axis) Index of new orders in non-durable goods sales direction total Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in mining of coal and lignite Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in extraction of crude petroleum and natural gas Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in other mining and quarrying Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in mining support service activities Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of food products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of beverages Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of tobacco products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of textiles Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of wearing apparel Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of leather and related products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of wood and of products of wood and cork except furniture Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of paper and paper products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in printing and reproduction of recorded media Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of coke and refined petroleum products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of chemicals and chemical products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of basic pharmaceuticcal products and pharmaceutical preparationsMrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of rubber and plastic products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of other non-metallic mineral products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of basic metals Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of fabricated metal products except machinery and equipment Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of computer electronic and optical products Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of electrical equipment Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of machinery and equipment nec Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of motor vehicles trailers and semi-trailers Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of other transport equipment Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in manufacture of furniture Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in other manufacturing Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Index of production in repair and installation of machinery and equipment Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70

(x-axis) Truck-toll-mileage index Apr 19(y-axis) RWIISL-Container-Throughput-Index Feb 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71

(x-axis) Truck-toll-mileage index Mrz 19(y-axis) Production index for flat pallets (GP71624110) Mrz 19

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72

(x-axis) Truck-toll-mileage index (quarterly) Jan 19(y-axis) Index of the gross domestic product Okt 18

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73

(x-axis) Truck-toll-mileage index (quarterly) Jan 19(y-axis) Index of services for freigt transport Okt 18

Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1

Annex C Annex C provides the estimation results for all models applied in the project Table C1 contains

the seasonally adjusted month-on-month rates of the Industrial Production Index for

manufacturing as released in May 2019 (Yt in equation 1) in the first column This dataset was

applied to develop the functional relationship in the support span and used as reference in the

estimation span The other columns contain the regressors as described in table 2 Table C2

contains the nowcasted seasonally adjusted month-on-month rates for the models f1 to f10 (YtSA

in equation 2) Quarterly data was assigned to the second month of the quarter Table C3

provides the same data for the reference models The respective nowcast errors (119864119905 in equation

3) can be found in the tables C4 and C5

C2

Table C1 Realised values of Industrial Production Index and the applied regressors

Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101

TO CI CODate IPI MI BC OI CT

C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5

Table C2 Nowcasts of model f1 to f10

Date f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6

Table C3 Nowcasts of model RM1 to RM810

Date RM1 RM2 RM3 RM4 RM5 RM6 RM7 RM8

Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7

Table C4 Nowcast errors of model f1 to f10


Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8

Table C5 Nowcast errors of model RM1 to RM8

C9

Datum RM1 RM2 RM3 RM4 RM5 RM6 RM7 RM8

Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data


2 Relationship between mileage and industrial production






3 Truck-toll-mileage index as a new building block of official short-term statistics





4 Conclusions and categorisation of the project

Literature

Legal basis

Copyright

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2

Executive summary

The project has carried out a feasibility study using statistical methods to decide whether the

Truck-Toll-Mileage Index could be used to continuously calculate a nowcast of the Industrial

Production Index in a ldquomechanisticrdquo way without requiring a resource intensive monthly expert

judgment assessing all kind of information available on the economic development

The Industrial Production Index and the Truck-Toll-Mileage Index were first presented as part of

the project Much of the goods traffic on roads can be assigned to the federal motorways

meaning that the Truck-Toll-Mileage Index provides a good indicator of total road freight

transport The index is calculated at the German Federal Office for Goods Transport and provided

monthly to the German Federal Statistical Office for seasonal adjustment and final publication as

standalone business cycle indicator The seasonal adjustment and publication procedures in the

Federal Statistical Office where accomplished in March 2019 For the Truck-Toll-Mileage Index

the publication timelag is 15 days after the end of the reporting month and can possibly be

reduced to 7 days in the future

Furthermore the project analysed the statistical relationship between the Truck-Toll-Mileage

Index and Industrial Production Index in a descriptive way The non-seasonally adjusted values

show a strong correlation between the month-on-month rates of the two indices A clear

correlation can also be observed with domestic trade certain service sectors and the overall

economy However for the Industrial Production Index the decomposition of the data into time

series components has shown that the close relationship between mileage and production

applies especially to the seasonal and calendar components There is less accordance in mileage

and production when trend movements or irregular fluctuations are regarded The seasonally

adjusted result is composed of the trend component and the irregular component Therefore the

relationship between production and the mileage is less pronounced when looking at the

seasonally adjusted month-on-month rates of the indices An analysis of cyclical trend

movements however indicates that business cycle developments are certainly reflected in the

development of mileage in some cases showing clear accordance in the economic turning

points In the years 2015 and 2016 there is less coincidence structural changes are likely to play

a role here

The question of whether the Truck-Toll-Mileage Index can be used to estimate a mechanistic

nowcast for the Industrial Production Index has further been considered Such a nowcast should

refer to the seasonally adjusted month-on-month rate of the Industrial Production Index as this

figure is in the focus of the German press releases To proceed a nowcast estimation procedure

has been developed which is based on the seasonal adjusted month-on-month rates of the

indices Alternative approaches referring to unadjusted growth rates with subsequent seasonal

adjustment where rejected The nowcast method used is the in science widely recognized

RegARMA modeling method Seven regressors have been formed in order to be able to include

further information in addition to the Truck-Toll-Mileage Index as independent variables

Through various combinations of regressors nine different RegARMA models f1 to f9 were

developed which were tested against each other in the project For each of these models the

RegARMA procedure was applied rolling by extending the support span by one month and thus

adding one estimation in the estimation span In this way 48 single nowcasts were produced for

each monthly model A tenth model was added which refers to quarterly data accounting for the

3


Production Index





















quarterly data

















4


Contents 4


















6 Conclusion 29


Annex 30

5











ME mean error




OI New Orders Index







6






































7

30

50

70

90

110

130

150


50

70

90

110

130

150
















Intermediate goods

Capital goods













8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE















LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














Literature

Legal basis

Copyright

sammelmappe_1

dashboard_5_6

dashboard_5_7

dashboard_5_8

dashboard_5_9

dashboard_5_10

dashboard_5_11

dashboard_5_12

dashboard_5_13

dashboard_5_14

dashboard_5_15

dashboard_5_16

dashboard_5_17

dashboard_5_18

dashboard_5_19

dashboard_5_20

dashboard_5_21

dashboard_5_22

dashboard_5_23

dashboard_5_24

dashboard_5_25

dashboard_5_26

dashboard_5_27

dashboard_5_28

dashboard_5_29

dashboard_5_30

dashboard_5_31

dashboard_5_32

dashboard_5_33

dashboard_5_34

dashboard_5_35

dashboard_5_36

dashboard_5_37

dashboard_5_38

dashboard_5_39

dashboard_5_40

dashboard_5_41

dashboard_5_42

dashboard_5_43

dashboard_5_44

dashboard_5_45

dashboard_5_46

dashboard_5_47

dashboard_5_48

dashboard_5_49

dashboard_5_51

dashboard_5_52

dashboard_5_53

dashboard_5_54

dashboard_5_55

dashboard_5_56

dashboard_5_57

dashboard_5_58

dashboard_5_59

dashboard_5_60

dashboard_5_61

dashboard_5_62

dashboard_5_63

dashboard_5_64

dashboard_5_65

dashboard_5_66

dashboard_5_67

dashboard_5_68

dashboard_5_69

dashboard_5_70

dashboard_5_71

dashboard_5_72

dashboard_5_73

dashboard_5_74

dashboard_5_75

dashboard_5_76

dashboard_5_4_a

dashboard_5_4_b

dashboard_1_2

dashboard_1_3

3


Production Index





















quarterly data

















4


Contents 4


















6 Conclusion 29


Annex 30

5











ME mean error




OI New Orders Index







6






































7

30

50

70

90

110

130

150


50

70

90

110

130

150
















Intermediate goods

Capital goods













8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE















LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














Literature

Legal basis

Copyright

sammelmappe_1

dashboard_5_6

dashboard_5_7

dashboard_5_8

dashboard_5_9

dashboard_5_10

dashboard_5_11

dashboard_5_12

dashboard_5_13

dashboard_5_14

dashboard_5_15

dashboard_5_16

dashboard_5_17

dashboard_5_18

dashboard_5_19

dashboard_5_20

dashboard_5_21

dashboard_5_22

dashboard_5_23

dashboard_5_24

dashboard_5_25

dashboard_5_26

dashboard_5_27

dashboard_5_28

dashboard_5_29

dashboard_5_30

dashboard_5_31

dashboard_5_32

dashboard_5_33

dashboard_5_34

dashboard_5_35

dashboard_5_36

dashboard_5_37

dashboard_5_38

dashboard_5_39

dashboard_5_40

dashboard_5_41

dashboard_5_42

dashboard_5_43

dashboard_5_44

dashboard_5_45

dashboard_5_46

dashboard_5_47

dashboard_5_48

dashboard_5_49

dashboard_5_51

dashboard_5_52

dashboard_5_53

dashboard_5_54

dashboard_5_55

dashboard_5_56

dashboard_5_57

dashboard_5_58

dashboard_5_59

dashboard_5_60

dashboard_5_61

dashboard_5_62

dashboard_5_63

dashboard_5_64

dashboard_5_65

dashboard_5_66

dashboard_5_67

dashboard_5_68

dashboard_5_69

dashboard_5_70

dashboard_5_71

dashboard_5_72

dashboard_5_73

dashboard_5_74

dashboard_5_75

dashboard_5_76

dashboard_5_4_a

dashboard_5_4_b

dashboard_1_2

dashboard_1_3

4


Contents 4


















6 Conclusion 29


Annex 30

5











ME mean error




OI New Orders Index







6






































7

30

50

70

90

110

130

150


50

70

90

110

130

150
















Intermediate goods

Capital goods













8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE















LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














Literature

Legal basis

Copyright

sammelmappe_1

dashboard_5_6

dashboard_5_7

dashboard_5_8

dashboard_5_9

dashboard_5_10

dashboard_5_11

dashboard_5_12

dashboard_5_13

dashboard_5_14

dashboard_5_15

dashboard_5_16

dashboard_5_17

dashboard_5_18

dashboard_5_19

dashboard_5_20

dashboard_5_21

dashboard_5_22

dashboard_5_23

dashboard_5_24

dashboard_5_25

dashboard_5_26

dashboard_5_27

dashboard_5_28

dashboard_5_29

dashboard_5_30

dashboard_5_31

dashboard_5_32

dashboard_5_33

dashboard_5_34

dashboard_5_35

dashboard_5_36

dashboard_5_37

dashboard_5_38

dashboard_5_39

dashboard_5_40

dashboard_5_41

dashboard_5_42

dashboard_5_43

dashboard_5_44

dashboard_5_45

dashboard_5_46

dashboard_5_47

dashboard_5_48

dashboard_5_49

dashboard_5_51

dashboard_5_52

dashboard_5_53

dashboard_5_54

dashboard_5_55

dashboard_5_56

dashboard_5_57

dashboard_5_58

dashboard_5_59

dashboard_5_60

dashboard_5_61

dashboard_5_62

dashboard_5_63

dashboard_5_64

dashboard_5_65

dashboard_5_66

dashboard_5_67

dashboard_5_68

dashboard_5_69

dashboard_5_70

dashboard_5_71

dashboard_5_72

dashboard_5_73

dashboard_5_74

dashboard_5_75

dashboard_5_76

dashboard_5_4_a

dashboard_5_4_b

dashboard_1_2

dashboard_1_3

5











ME mean error




OI New Orders Index







6






































7

30

50

70

90

110

130

150


50

70

90

110

130

150
















Intermediate goods

Capital goods













8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE















LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














Literature

Legal basis

Copyright

sammelmappe_1

dashboard_5_6

dashboard_5_7

dashboard_5_8

dashboard_5_9

dashboard_5_10

dashboard_5_11

dashboard_5_12

dashboard_5_13

dashboard_5_14

dashboard_5_15

dashboard_5_16

dashboard_5_17

dashboard_5_18

dashboard_5_19

dashboard_5_20

dashboard_5_21

dashboard_5_22

dashboard_5_23

dashboard_5_24

dashboard_5_25

dashboard_5_26

dashboard_5_27

dashboard_5_28

dashboard_5_29

dashboard_5_30

dashboard_5_31

dashboard_5_32

dashboard_5_33

dashboard_5_34

dashboard_5_35

dashboard_5_36

dashboard_5_37

dashboard_5_38

dashboard_5_39

dashboard_5_40

dashboard_5_41

dashboard_5_42

dashboard_5_43

dashboard_5_44

dashboard_5_45

dashboard_5_46

dashboard_5_47

dashboard_5_48

dashboard_5_49

dashboard_5_51

dashboard_5_52

dashboard_5_53

dashboard_5_54

dashboard_5_55

dashboard_5_56

dashboard_5_57

dashboard_5_58

dashboard_5_59

dashboard_5_60

dashboard_5_61

dashboard_5_62

dashboard_5_63

dashboard_5_64

dashboard_5_65

dashboard_5_66

dashboard_5_67

dashboard_5_68

dashboard_5_69

dashboard_5_70

dashboard_5_71

dashboard_5_72

dashboard_5_73

dashboard_5_74

dashboard_5_75

dashboard_5_76

dashboard_5_4_a

dashboard_5_4_b

dashboard_1_2

dashboard_1_3

6






































7

30

50

70

90

110

130

150


50

70

90

110

130

150
















Intermediate goods

Capital goods













8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE





Deutsche Bundesbank Monatsbericht Mai 2010 Frankfurt 2010 6 f [Accessed on 22 October 2018] Available at wwwbundesbankde










LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














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dashboard_5_58

dashboard_5_59

dashboard_5_60

dashboard_5_61

dashboard_5_62

dashboard_5_63

dashboard_5_64

dashboard_5_65

dashboard_5_66

dashboard_5_67

dashboard_5_68

dashboard_5_69

dashboard_5_70

dashboard_5_71

dashboard_5_72

dashboard_5_73

dashboard_5_74

dashboard_5_75

dashboard_5_76

dashboard_5_4_a

dashboard_5_4_b

dashboard_1_2

dashboard_1_3

7

30

50

70

90

110

130

150


50

70

90

110

130

150
















Intermediate goods

Capital goods













8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE















LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














Literature

Legal basis

Copyright

sammelmappe_1

dashboard_5_6

dashboard_5_7

dashboard_5_8

dashboard_5_9

dashboard_5_10

dashboard_5_11

dashboard_5_12

dashboard_5_13

dashboard_5_14

dashboard_5_15

dashboard_5_16

dashboard_5_17

dashboard_5_18

dashboard_5_19

dashboard_5_20

dashboard_5_21

dashboard_5_22

dashboard_5_23

dashboard_5_24

dashboard_5_25

dashboard_5_26

dashboard_5_27

dashboard_5_28

dashboard_5_29

dashboard_5_30

dashboard_5_31

dashboard_5_32

dashboard_5_33

dashboard_5_34

dashboard_5_35

dashboard_5_36

dashboard_5_37

dashboard_5_38

dashboard_5_39

dashboard_5_40

dashboard_5_41

dashboard_5_42

dashboard_5_43

dashboard_5_44

dashboard_5_45

dashboard_5_46

dashboard_5_47

dashboard_5_48

dashboard_5_49

dashboard_5_51

dashboard_5_52

dashboard_5_53

dashboard_5_54

dashboard_5_55

dashboard_5_56

dashboard_5_57

dashboard_5_58

dashboard_5_59

dashboard_5_60

dashboard_5_61

dashboard_5_62

dashboard_5_63

dashboard_5_64

dashboard_5_65

dashboard_5_66

dashboard_5_67

dashboard_5_68

dashboard_5_69

dashboard_5_70

dashboard_5_71

dashboard_5_72

dashboard_5_73

dashboard_5_74

dashboard_5_75

dashboard_5_76

dashboard_5_4_a

dashboard_5_4_b

dashboard_1_2

dashboard_1_3

8

30

50

70

90

110

130

150


50

70

90

110

130

150




















Intermediate goods

Capital goods









9






Industrial

Production

Index

= 100










































10




















11

30

50

70

90

110

130

150


unadjusted MI





























reasonable high


12

-03

-01

01

03

-03 -015 0 015 03

r = 085

-03

-01

01

03

-03 -015 0 015 03

r = 057
































irregular component


13





































14

-015

0

015


tre

nd

de

via

tio

n in

































15

-015

0

015


tre

nd

de

via

tio

n in
































16











following equation8

(1) Yt = I t

Itminus1minus 1

























in figure 9


17







December 20189






May 2019

















Y120

Y119





Y120

Y1 hellip

Y121 Y121

Y1 hellip

Y122

Y166

Y1 hellip

Y167

hellip


18





adjusted nowcast


SA x2tSA hellip )


U minus ( st


U = f U(x1tU x2t

U hellip )






















in the further









19
















0 months


0 months


0 months




















20






































21








































22






Tsum |Et|

T

t=1

smaller is better


Tsum Et

2T

t=1

smaller is better


better


better


PWS = sum s


Yt

Ytlt 0 else s=0

smaller is better


median of 119884119905 PHM =

sum h


smaller is better

Q7 Pcom


median of Yt

Pcom = sum z


Yt

Ytlt

0 else z = 0

smaller is better


1T

sum (Yt

minus 119905)2T

t=1

1T

sum (Yt minus Y)2t

smaller is

better


Tsum Et

T

t=1


better sign gives


overestimation















past



23








f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q1 MAE 087 086 085 085 082 082 083 084 084 109

Q2 RMSE 105 103 102 104 099 100 100 103 101 130

Q3 MinAE 001 000 001 001 004 005 005 001 001 026

Q4 MaxAE 224 236 266 265 231 225 240 264 265 267

Q5 PWS 3542 292 2292 2500 3333 3125 3125 250 2292 4375

Q6 PHM 5208 5000 4792 3958 3958 4167 4583 4167 5417 5000

Q7 Pcom 1042 625 625 625 1042 1250 833 625 625 625

Q8 NSR 056 054 053 056 050 051 051 054 052 169

Q9 ME -016 -029 -036 -043 -030 -026 -027 -042 -038 -075





















24












f2 to -036 in f3






























25




























MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf1

f2

f3

f4

f5

f6

f7

f8

f9

26














75

90

105




27

























Index













28











Q1 MAE 086 107 099 109 099 113 106 109 082 099

Q2 RMSE 103 137 131 138 131 144 143 130 116 130

Q3 MinAE 000 000 002 001 000 009 001 026 007 011

Q4 MaxAE 236 445 374 440 355 467 348 267 295 264

Q5 PWS 292 4167 3958 4583 3542 6042 3542 4375 2500 3125

Q6 PHM 5000 4792 4792 4583 4583 5625 5000 5000 375 4375

Q7 Pcom 625 2292 1875 1667 2083 2292 1875 625 625 125

Q8 NSR 054 096 087 098 087 105 104 169 134 168

Q9 ME -029 008 007 001 003 002 005 -075 012 -042


MAE

RMSE

MaxAE

PWS

PHM

Pcom

NSR

MEf2

RM1

RM2

RM3

RM4

RM5

RM6

f10

RM7

RM8

29

















1

Tsum Et

2Tt=1 radic

1

Tsum ERt

2Tt=1frasl

R reference model




f1 f2 f3 f4 f5 f6 f7 f8 f9 f10

Q10 TIM 080 079 078 079 076 076 076 079 077 112




6 Conclusion














30























Annex










ABSTRACT










other traffic data





aviation markets







mentrdquo


1


11 Truck toll data





Excursus






































2019 - 01 - 0091

0

1

2

3

4
















2019 - 01 - 0092

80

100

120

140

160

180






2













-30

-20

-10

0

10

20

30



r = 086








-30

-20

-10

0

10

20

30



r = 097



-30

-20

-10

0

10

20

30



r = 063

-30

-20

-10

0

10

20

30



r = 091

-30

-20

-10

0

10

20

30



r = 080



























2019 - 01 - 0095

08

09

10

11









-20

-10

0

10

20



r = 098



-20

-10

0

10

20



r = 099

-20

-10

0

10

20



r = 085

-20

-10

0

10

20



r = 036














-20

-10

0

10

20



r = 054











2019 - 01 - 0098

-015

-010

-005

005

010

015

0


Feb 08

Jul 09

May 11

Mar 13Feb 14

Dec 17

Jan 18

Jun 11

Jan 14


Mar 13

Jul 09

Feb 08









-015

-010

-005

005

010

015

0


Nov 17



Jul 09

Dec 17

Feb 13

Jun 11

Jan 08

Mar 13

May 11

Feb 08

Jul 09











3












2019 - 01 - 0100





























4








LITERATURE















LITERATURE





LEGAL BASIS






wwwdestatisde


Abbreviations

WISTA

JD

D

Vj

Hj

a n g

o a S

St

Mill

Mrd


= annual average


= quarter of a year

= half-year



= piece

= million

= billion













Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 065

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 037

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B1

ANNEX - B


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 072

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 033

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B2


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 095

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 025

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B3


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 036

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B4


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 039

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B5


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 082

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B6


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 010

0

100

200

300

0 100 200

Rsup2 = 049

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 032

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

-02 0 02

0

50

100

150

200

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

180

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B7


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 049

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B8


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 030

-08-06-04-02

002040608

11214

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 014

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B9


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 001

-08-06-04-02

00204

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

95

100

105

110

115

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B10


Seasonally adjusted


Cross-correlation




trade

Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 058

0

50

100

150

0 100 200

Rsup2 = 049

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 028

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B11


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B12


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 051

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 044

0

50

100

150

0 100 200

Rsup2 = 005

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B13


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 040

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B14


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 031

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B15


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 091

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 026

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B16


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 084

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B17


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 022

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B18


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 025

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 006

95

100

105

110

115

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B19


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 026

0

100

200

300

400

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 039

0

100

200

300

0 100 200

Rsup2 = 001

-04

-02

0

02

04

06

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B20


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 068

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 031

0

50

100

150

0 100 200

Rsup2 = 032

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B21


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 093

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 046

0

50

100

150

0 100 200

Rsup2 = 023

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B22


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B23


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 006

0

50

100

150

200

0 100 200

Rsup2 = 066

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B24


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

0 100 200

Rsup2 = 046

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 017

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B25


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 000

0

50

100

150

200

0 100 200

Rsup2 = 023

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 000

-06-04-02

0020406

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B26


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 063

0

50

100

150

0 100 200

Rsup2 = 061

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B27


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 080

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B28


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 029

-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 032

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B29


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 063

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B30


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 061

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 059

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B31


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

200

0 100 200

Rsup2 = 004

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 016

0

50

100

150

200

0 100 200

Rsup2 = 002

-06-04-02

002040608

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B32


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 067

0

50

100

150

0 100 200

Rsup2 = 034

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B33


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 079

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 087

0

50

100

150

0 100 200

Rsup2 = 019

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B34


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 054

0

50

100

150

0 100 200

Rsup2 = 018

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 009

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B35


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 042

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B36


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 077

0

50

100

150

0 100 200

Rsup2 = 068

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 007

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B37


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 059

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 071

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B38


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 083

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 057

0

50

100

150

0 100 200

Rsup2 = 015

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B39


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 050

0

50

100

150

0 100 200

Rsup2 = 024

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B40


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 069

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 070

0

50

100

150

0 100 200

Rsup2 = 009

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B41


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 045

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 005

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B42


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 020

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 063

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-050

-040

-030

-020

-010

000

010

020

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B43


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 022

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 000

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

250

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

025

030

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B44


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 053

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B45


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 004

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 005

0

100

200

300

0 100 200

Rsup2 = 000

-06-04-02

002040608

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-040

-020

000

020

040

060

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B46


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 034

0

50

100

150

0 100 200

Rsup2 = 054

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 056

85

90

95

100

105

110

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B47


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 017

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 009

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B48


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

350

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

400

0 100 200

Rsup2 = 059

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 002

-04

-02

0

02

04

-02 0 02

0

50

100

150

200

250

300

2005 2009 2013 2017

0

50

100

150

200

250

300

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B49


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 011

0

50

100

150

0 100 200

Rsup2 = 078

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 001

0

50

100

150

0 100 200

Rsup2 = 020

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B50


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

250

300

2005 2009 2013 2017

Rsup2 = 012

0

100

200

300

0 100 200

Rsup2 = 020

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

200

250

2005 2009 2013 2017

Rsup2 = 034

0

100

200

300

0 100 200

Rsup2 = 003

-04

-02

0

02

-02 0 02

0

50

100

150

200

250

2005 2009 2013 2017

0

50

100

150

200

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B51


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 065

0

50

100

150

0 100 200

Rsup2 = 063

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 001

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B52


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 055

0

50

100

150

0 100 200

Rsup2 = 071

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 012

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B53


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 036

0

50

100

150

0 100 200

Rsup2 = 089

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

85

90

95

100

105

110

0 100 200

Rsup2 = 024

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 600

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B54


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 002

0

50

100

150

0 100 200

Rsup2 = 031

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 045

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B55


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 005

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 012

0

50

100

150

0 100 200

Rsup2 = 000

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B56


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 015

0

50

100

150

0 100 200

Rsup2 = 061

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 004

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B57


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

200

0 100 200

Rsup2 = 064

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 074

0

50

100

150

200

0 100 200

Rsup2 = 002

-04

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B58


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 081

0

50

100

150

0 100 200

Rsup2 = 082

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 079

0

50

100

150

0 100 200

Rsup2 = 011

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B59


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 043

0

50

100

150

0 100 200

Rsup2 = 065

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 047

0

50

100

150

0 100 200

Rsup2 = 018

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B60


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 029

0

50

100

150

0 100 200

Rsup2 = 076

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 010

0

50

100

150

0 100 200

Rsup2 = 010

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B61


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 090

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 086

0

50

100

150

0 100 200

Rsup2 = 022

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B62


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 064

0

50

100

150

0 100 200

Rsup2 = 032

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 002

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B63


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 088

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 053

0

50

100

150

0 100 200

Rsup2 = 016

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B64


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 030

0

50

100

150

0 100 200

Rsup2 = 008

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 062

0

50

100

150

0 100 200

Rsup2 = 014

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B65


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 062

-06-04-02

002040608

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 060

0

50

100

150

0 100 200

Rsup2 = 006

-04

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-025

-020

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B66


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 066

0

50

100

150

0 100 200

Rsup2 = 080

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 008

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B67


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 008

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

06

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 003

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

-10

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6-10

00

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B68


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 057

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 083

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B69


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

200

2005 2009 2013 2017

Rsup2 = 014

0

50

100

150

200

0 100 200

Rsup2 = 000

-06-04-02

0020406

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 072

0

50

100

150

0 100 200

Rsup2 = 001

-02

0

02

04

-02 0 02

0

20

40

60

80

100

120

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B70


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 042

0

50

100

150

0 100 200

Rsup2 = 006

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 069

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

0

20

40

60

80

100

120

140

160

2005 2009 2013 2017

-015

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B71


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 075

0

50

100

150

0 100 200

Rsup2 = 081

-04

-02

0

02

04

-04 -02 0 02 04

0

50

100

150

2005 2009 2013 2017

Rsup2 = 085

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-030

-025

-020

-015

-010

-005

000

005

010

015

020

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B72


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 088

0

50

100

150

0 100 200

Rsup2 = 045

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 089

0

50

100

150

0 100 200

Rsup2 = 071

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

140

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B73


Seasonally adjusted


Cross-correlation



Unadjusted values

0

50

100

150

2005 2009 2013 2017

Rsup2 = 068

0

50

100

150

0 100 200

Rsup2 = 004

-02

0

02

-02 0 02

0

50

100

150

2005 2009 2013 2017

Rsup2 = 078

0

50

100

150

0 100 200

Rsup2 = 003

-02

0

02

-02 0 02

0

20

40

60

80

100

120

140

2005 2009 2013 2017

0

20

40

60

80

100

120

2005 2009 2013 2017

-010

-005

000

005

010

015

2005 2009 2013 2017

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

00

10

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

-05

05

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

BV4 Trend

B74

C1










C2


Jan 05 844 790 969 824 078 089 108

Feb 05 831 803 966 809 078 091 107

Mrz 05 842 808 939 825 080 094 106

Apr 05 848 821 929 818 080 092 109

Mai 05 837 821 924 823 080 091 103

Jun 05 855 827 926 85 081 094 108

Jul 05 868 837 949 869 080 094 106

Aug 05 843 833 953 849 082 091 104

Sep 05 867 848 964 876 082 095 103

Okt 05 884 870 987 888 082 093 104

Nov 05 874 867 980 90 084 093 106

Dez 05 872 873 991 895 084 093 109

Jan 06 877 856 1004 902 084 092 110

Feb 06 882 868 1016 906 085 093 108

Mrz 06 874 867 1030 898 086 093 108

Apr 06 894 892 1030 921 086 091 112

Mai 06 907 910 1030 925 086 091 112

Jun 06 902 909 1036 911 086 091 110

Jul 06 918 920 1026 937 085 093 113

Aug 06 924 921 1030 968 088 090 115

Sep 06 923 926 1038 952 086 092 113

Okt 06 921 935 1046 947 086 091 112

Nov 06 937 944 1054 951 086 094 112

Dez 06 946 954 1070 958 085 090 118

Jan 07 951 953 1062 97 880 089 092 111

Feb 07 955 952 1055 999 949 089 092 112

Mrz 07 960 967 1064 999 894 088 092 109

Apr 07 951 944 1065 988 917 089 091 104

Mai 07 968 962 1065 1019 907 090 093 106

Jun 07 967 969 1060 1056 911 091 093 110

Jul 07 974 964 1060 1008 906 090 094 110

Aug 07 977 968 1053 1014 912 089 093 108

Sep 07 987 978 1047 1014 933 091 093 107

Okt 07 988 982 1051 1059 938 092 095 108

Nov 07 986 980 1047 1078 949 091 095 108

Dez 07 996 989 1038 1068 968 092 093 107

Jan 08 1011 1014 1034 1042 970 088 096 105

Feb 08 1008 1010 1023 1045 991 091 095 108

Mrz 08 1001 998 1024 1028 986 091 093 109

Apr 08 1003 985 1009 1033 988 092 095 111

Mai 08 983 981 1007 1007 978 091 093 105

Jun 08 994 965 982 972 984 091 095 111

Jul 08 977 969 956 965 977 090 094 105

Aug 08 997 966 920 984 978 089 094 107

Sep 08 974 968 900 919 966 090 096 106

Okt 08 953 956 859 859 980 091 094 103

Nov 08 911 918 798 796 932 089 098 103

Dez 08 877 905 748 742 891 087 102 093

Jan 09 807 866 752 686 817 080 096 091

Feb 09 782 851 733 665 861 082 091 090

Mrz 09 785 836 725 692 857 081 096 092

Apr 09 762 846 747 688 834 077 092 090

Mai 09 797 842 751 714 841 080 095 092

Jun 09 803 850 778 743 841 081 094 091

Jul 09 795 849 800 772 850 084 090 092

Aug 09 805 861 834 78 861 082 089 092

Sep 09 841 871 857 806 883 086 092 092

Okt 09 823 860 870 786 902 085 088 094

Nov 09 833 854 893 809 911 086 087 096

Dez 09 833 877 898 796 937 088 088 094

Jan 10 841 864 907 825 951 088 088 097

Feb 10 833 865 909 827 984 088 087 097

Mrz 10 861 890 935 866 967 086 089 094

Apr 10 877 895 973 892 983 088 087 097

Mai 10 904 901 989 896 994 090 089 101

Jun 10 901 910 1002 916 1004 088 089 100

Jul 10 895 914 1034 909 1016 089 087 099

Aug 10 910 912 1051 933 1023 090 088 100

Sep 10 924 917 1049 92 1011 090 091 098

Okt 10 944 920 1064 924 1009 090 092 103

Nov 10 939 920 1075 973 1036 091 092 099

Dez 10 954 913 1071 942 1022 096 097 101


C3

Jan 11 952 923 1067 984 1049 094 091 101

Feb 11 963 932 1075 996 1059 091 092 100

Mrz 11 969 941 1072 963 1042 092 093 101

Apr 11 975 942 1065 978 1072 090 092 101

Mai 11 987 931 1066 1009 1073 089 095 102

Jun 11 971 935 1064 1003 1071 090 093 095

Jul 11 1001 943 1049 982 1082 090 095 107

Aug 11 993 944 1019 978 1075 091 095 102

Sep 11 978 936 997 942 1080 090 095 102

Okt 11 989 947 992 957 1090 089 096 107

Nov 11 982 948 981 925 1068 090 096 103

Dez 11 968 923 987 943 1103 090 097 100

Jan 12 973 918 996 93 1095 089 097 104

Feb 12 972 921 1001 938 1092 092 097 101

Mrz 12 984 938 999 959 1107 092 099 098

Apr 12 966 920 1006 938 1108 092 096 102

Mai 12 985 939 982 953 1118 092 098 103

Jun 12 973 934 962 928 1112 092 096 099

Jul 12 984 933 941 936 1108 092 098 102

Aug 12 984 936 941 935 1103 094 098 102

Sep 12 971 940 929 913 1164 092 098 099

Okt 12 958 932 922 944 1117 093 096 097

Nov 12 951 920 922 917 1115 092 098 097

Dez 12 959 927 930 923 1134 095 099 094

Jan 13 947 926 955 922 1154 093 096 097

Feb 13 955 921 974 941 1119 094 099 098

Mrz 13 968 916 970 959 1129 096 098 100

Apr 13 971 934 958 933 1126 097 100 099

Mai 13 965 938 966 937 1136 095 096 095

Jun 13 982 942 981 979 1134 095 099 103

Jul 13 965 951 985 956 1142 093 096 100

Aug 13 987 952 1004 964 1148 094 101 098

Sep 13 982 947 1007 99 1151 094 099 101

Okt 13 975 954 1009 977 1145 095 096 097

Nov 13 997 958 1024 988 1149 097 100 098

Dez 13 998 958 1019 977 1144 097 099 095

Jan 14 993 969 1030 995 1150 098 099 095

Feb 14 994 959 1036 995 1169 097 098 098

Mrz 14 998 976 1036 973 1183 096 099 099

Apr 14 994 975 1036 996 1194 095 097 095

Mai 14 987 954 1027 961 1184 098 100 098

Jun 14 988 969 1015 959 1195 099 098 097

Jul 14 1009 983 1010 1023 1190 097 101 098

Aug 14 967 959 997 971 1202 098 096 094

Sep 14 991 968 984 985 1202 099 100 097

Okt 14 993 969 956 1006 1216 099 099 097

Nov 14 994 966 964 983 1202 100 100 099

Dez 14 1011 988 980 1016 1197 100 100 100

Jan 15 989 988 989 1002 1196 100 099 098

Feb 15 995 984 995 989 1203 101 100 102

Mrz 15 995 988 1006 994 1199 098 099 098

Apr 15 1001 984 1017 1012 1195 101 100 101

Mai 15 1002 997 1013 998 1186 100 101 102

Jun 15 999 995 1000 1034 1187 099 098 095

Jul 15 1014 1003 1004 1015 1187 100 102 097

Aug 15 989 999 1001 994 1183 099 099 101

Sep 15 992 1003 991 98 1178 100 100 100

Okt 15 1000 999 992 986 1182 100 102 102

Nov 15 992 1003 996 994 1179 100 099 102

Dez 15 1000 1024 995 977 1179 101 099 101

Jan 16 1021 1033 982 1004 1183 100 102 099

Feb 16 1019 1040 961 998 1182 100 099 101

Mrz 16 1007 1036 970 1013 1182 100 100 100

Apr 16 1014 1041 973 1007 1200 101 101 102

Mai 16 994 1025 981 1003 1203 102 098 098

Jun 16 1013 1030 991 1007 1213 101 102 104

Jul 16 998 1032 989 1007 1207 101 100 102

Aug 16 1017 1042 980 1011 1212 102 101 102

Sep 16 1015 1040 999 1003 1219 102 101 100

Okt 16 1018 1054 1013 1032 1229 104 102 104

Nov 16 1019 1061 1007 1013 1249 103 101 101

Dez 16 1001 1049 1010 1054 1256 100 098 106


C4

Jan 17 1019 1043 1015 101 1244 103 100 105

Feb 17 1030 1076 1030 1065 1262 102 100 103

Mrz 17 1027 1072 1043 1054 1275 103 100 101

Apr 17 1037 1082 1054 1066 1282 103 099 106

Mai 17 1042 1080 1067 1048 1286 103 101 105

Jun 17 1037 1085 1068 1066 1291 103 101 103

Jul 17 1046 1087 1081 1056 1298 105 099 104

Aug 17 1069 1101 1077 1093 1306 104 102 107

Sep 17 1060 1101 1075 1098 1320 105 100 103

Okt 17 1039 1063 1086 1106 1311 105 098 105

Nov 17 1086 1125 1095 1107 1318 107 101 102

Dez 17 1078 1103 1083 113 1315 106 099 103

Jan 18 1075 1124 1093 1094 1336 105 101 101

Feb 18 1055 1114 1077 1102 1346 104 098 100

Mrz 18 1070 1109 1066 1086 1305 105 102 101

Apr 18 1060 1101 1054 1079 1327 107 102 100

Mai 18 1080 1125 1053 1095 1360 106 101 097

Jun 18 1073 1120 1049 1066 1340 105 101 098

Jul 18 1053 1112 1043 1058 1353 106 099 096

Aug 18 1058 1116 1048 1074 1353 107 099 096

Sep 18 1057 1120 1046 1075 1348 108 100 098

Okt 18 1050 1124 1026 1076 1381 105 100 101

Nov 18 1038 1131 1019 1068 1350 104 100 102

Dez 18 1045 1144 1006 1079 1368 108 101 104


C5



Jan 15 00000 00018 00048 00047 00013 00008 00022 00046 00047

Feb 15 -00031 00046 00138 00127 00066 00053 00072 00125 00146 00153

Mrz 15 00031 00000 00057 00001 00016 00038 00005 00005 00046

Apr 15 -00031 -00023 00027 -00021 -00012 00013 -00009 -00018 00023

Mai 15 00102 00086 00113 00091 00080 00098 00079 00090 00115 00051

Jun 15 -00015 00026 00033 00025 00022 00044 00030 00023 00036

Jul 15 00061 00080 00048 00159 00073 00074 00080 00151 00078

Aug 15 -00031 -00067 -00079 -00069 -00065 -00062 -00050 -00071 -00082 00115

Sep 15 00031 00120 00130 00111 00111 00100 00138 00108 00125

Okt 15 -00031 -00035 -00033 -00040 -00021 -00033 -00017 -00036 -00041

Nov 15 00031 -00008 -00033 -00040 -00014 -00025 -00003 -00038 -00041 00074

Dez 15 00160 00242 00224 00237 00210 00190 00216 00230 00228

Jan 16 00067 00123 00133 00126 00110 00146 00103 00121 00130

Feb 16 00052 00013 -00039 00034 00005 00052 00001 00028 -00026 00310

Mrz 16 -00029 -00004 -00088 00013 00006 00007 00004 00011 -00076

Apr 16 00037 00076 00081 00144 00077 00083 00081 00137 00095

Mai 16 -00117 -00151 -00114 -00106 -00136 -00141 -00125 -00107 -00110 -00060

Jun 16 00038 00066 00108 00083 00074 00054 00082 00084 00106

Jul 16 00015 -00038 -00010 -00061 -00043 -00049 -00030 -00059 -00020

Aug 16 00075 00155 00162 00159 00153 00167 00156 00158 00166 00062

Sep 16 -00015 -00057 -00079 -00050 -00048 -00055 -00053 -00047 -00076

Okt 16 00105 00126 00140 00085 00124 00118 00120 00088 00121

Nov 16 00051 00098 00158 00152 00094 00085 00095 00147 00169 00177

Dez 16 -00087 -00084 -00041 -00044 -00072 -00039 -00057 -00045 -00033

Jan 17 -00044 -00025 00019 00098 -00008 -00007 00005 00090 00056

Feb 17 00244 00199 00167 00101 00184 00167 00168 00104 00130 00076

Mrz 17 -00028 00037 00065 00094 00034 00044 00040 00088 00086

Apr 17 00070 00082 00148 00132 00080 00103 00087 00125 00154

Mai 17 -00014 -00022 00035 00011 -00029 -00014 -00011 00004 00043 00199

Jun 17 00035 00017 00057 -00022 00007 00036 00023 -00023 00041

Jul 17 00014 00052 00079 00073 00065 00038 00058 00073 00086

Aug 17 00097 00089 00115 00069 00083 00055 00079 00070 00102 00136

Sep 17 00000 -00040 -00062 -00025 -00033 -00039 -00044 -00025 -00051

Okt 17 -00262 -00281 -00256 -00209 -00240 -00210 -00234 -00205 -00231

Nov 17 00437 00481 00453 00464 00448 00433 00416 00458 00449 00012

Dez 17 -00147 -00148 -00124 -00143 -00136 -00168 -00163 -00142 -00129

Jan 18 00142 00131 00091 00146 00141 00125 00099 00147 00099

Feb 18 -00065 -00001 00027 -00007 00007 00005 -00017 -00004 00015 00198

Mrz 18 -00033 00001 00022 00065 00018 -00025 -00009 00065 00040

Apr 18 -00053 -00135 -00170 -00158 -00118 -00153 -00153 -00150 -00178

Mai 18 00161 00204 00116 00134 00197 00151 00152 00140 00099 -00023

Jun 18 -00033 -00038 -00071 -00035 -00050 -00073 -00072 -00037 -00069

Jul 18 -00053 -00053 -00064 -00084 -00053 -00080 -00069 -00081 -00078

Aug 18 00027 00079 00078 00075 00079 00052 00059 00076 00073 00010

Sep 18 00027 00026 00047 00063 00040 -00004 00006 00065 00052

Okt 18 00027 00047 00053 00077 00058 00011 00041 00078 00060

Nov 18 00046 00092 00053 00134 00084 00065 00091 00128 00075 00157

Dez 18 00085 00164 00144 00212 00148 00169 00165 00200 00160

C6



Jan 15 00003 00014 -00002 00047 -00010 00130

Feb 15 -00002 00072 00013 00121 -00002 00031 00150 -00043

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 00000 00047 -00008 00061 -00008 -00039

Mai 15 00000 -00019 -00017 00049 -00008 00028 00063 -00077

Jun 15 00000 -00050 00002 -00010 -00018 -00028

Jul 15 00000 00012 00000 -00056 00002 00177

Aug 15 00000 -00038 -00008 -00032 00000 -00083 -00052 -00051

Sep 15 00002 00013 -00010 00054 -00008 00028

Okt 15 00000 -00012 00008 -00058 -00010 -00095

Nov 15 -00001 00001 -00006 -00030 00008 -00127 -00021 -00021

Dez 15 00001 00017 00000 00043 -00006 00053

Jan 16 -00001 -00067 00008 -00020 00000 -00070

Feb 16 -00001 -00127 -00002 -00118 00008 -00057 -00111 00018

Mrz 16 00000 00023 00000 -00110 -00002 -00007

Apr 16 00001 00037 00035 00050 00000 00164

Mai 16 -00001 00018 00006 00005 00036 -00039 00050 00179

Jun 16 00002 00080 00019 00092 00006 -00031

Jul 16 -00004 -00038 -00012 00010 00019 -00052

Aug 16 00004 -00003 00010 00043 -00011 00084 00037 00055

Sep 16 -00007 00020 00014 -00098 00009 -00023

Okt 16 00001 00066 00019 00084 00013 -00058

Nov 16 -00001 -00015 00038 00078 00019 00073 00094 00237

Dez 16 00000 00003 00013 -00011 00037 00010

Jan 17 00006 00056 -00022 00053 00012 00134

Feb 17 -00008 00014 00033 -00024 -00021 -00177 00086 00110

Mrz 17 -00004 00032 00024 00045 00032 00032

Apr 17 00001 00055 00013 00083 00022 00096

Mai 17 -00004 00027 00007 00041 00012 00040 00152 00181

Jun 17 -00002 00004 00009 00053 00007 -00124

Jul 17 00002 00052 00012 00035 00008 00034

Aug 17 -00003 -00023 00014 00031 00012 00019 00061 00147

Sep 17 -00008 -00057 00025 -00060 00014 00006

Okt 17 00003 00054 -00016 00010 00024 00050

Nov 17 00007 00078 00013 00097 -00015 00115 00045 00045

Dez 17 -00025 -00141 -00005 -00075 00011 -00202

Jan 18 00004 00041 00038 -00021 -00005 -00003

Feb 18 00002 -00038 00018 00038 00033 00094 -00040 00096

Mrz 18 00011 -00006 -00071 -00005 00015 00120

Apr 18 -00009 -00080 00037 -00109 -00061 -00120

Mai 18 00006 00010 00054 -00038 00034 -00055 -00113 00088

Jun 18 -00013 -00059 -00033 -00073 00052 -00081

Jul 18 00005 -00008 00022 -00001 -00030 -00111

Aug 18 00013 00058 00000 00022 00020 00055 -00026 00058

Sep 18 -00003 -00013 -00008 00003 00000 00046

Okt 18 00001 -00068 00054 00002 -00008 00030

Nov 18 00005 -00025 -00048 -00073 00051 00081 -00126 00096

Dez 18 00008 -00024 00029 -00024 -00045 00075

C7



Jan 15 -00218 00236 -00266 -00265 -00231 -00225 -00240 -00264 -00265

Feb 15 00092 -00014 -00077 -00066 -00006 00008 -00012 -00064 -00085 -00216

Mrz 15 -00031 00000 -00057 -00001 -00016 -00038 -00005 -00005 -00046

Apr 15 00091 -00084 00033 00081 00073 00047 00069 00078 00037

Mai 15 -00092 00076 -00103 -00081 -00070 -00088 -00069 -00080 -00105 00027

Jun 15 -00015 00056 -00063 -00055 -00052 -00074 -00060 -00053 -00066

Jul 15 00089 -00071 00102 -00009 00077 00076 00070 -00001 00072

Aug 15 -00216 00180 -00167 -00177 -00182 -00184 -00197 -00176 -00164 -00138

Sep 15 -00001 00090 -00099 -00080 -00081 -00070 -00108 -00078 -00095

Okt 15 00111 -00115 00114 00120 00102 00113 00098 00116 00121

Nov 15 -00111 00072 -00047 -00040 -00066 -00055 -00077 -00042 -00039 -00084

Dez 15 -00080 00161 -00144 -00156 -00130 -00109 -00135 -00149 -00148

Jan 16 00143 -00087 00077 00084 00100 00064 00107 00089 00080

Feb 16 -00071 00033 00019 -00054 -00025 -00071 -00021 -00048 00006 -00126

Mrz 16 -00088 00114 -00029 -00130 -00124 -00125 -00122 -00129 -00042

Apr 16 00033 00007 -00012 -00074 -00007 -00013 -00012 -00068 -00025

Mai 16 -00080 00046 -00083 -00091 -00061 -00056 -00072 -00090 -00088 -00026

Jun 16 00154 -00125 00083 00108 00117 00137 00110 00108 00085

Jul 16 -00163 00110 -00138 -00087 -00105 -00099 -00118 -00089 -00129

Aug 16 00116 -00036 00028 00031 00037 00023 00034 00032 00024 -00032

Sep 16 -00005 -00038 00059 00030 00029 00035 00033 00028 00056

Okt 16 -00075 00096 -00110 -00055 -00095 -00089 -00090 -00059 -00091

Nov 16 -00041 00089 -00148 -00142 -00084 -00075 -00085 -00137 -00159 -00151

Dez 16 -00089 00093 -00135 -00132 -00105 -00137 -00120 -00131 -00144

Jan 17 00224 -00205 00161 00082 00188 00187 00175 00090 00123

Feb 17 -00136 00091 -00059 00007 -00076 -00059 -00060 00004 -00022 00049

Mrz 17 -00001 00066 -00094 -00123 -00063 -00073 -00069 -00117 -00115

Apr 17 00027 -00015 -00050 -00034 00018 -00006 00010 -00028 -00057

Mai 17 00062 -00070 00014 00037 00078 00062 00059 00044 00005 -00069

Jun 17 -00083 00065 -00105 -00026 -00055 -00084 -00071 -00025 -00089

Jul 17 00073 -00035 00008 00014 00022 00048 00029 00013 00001

Aug 17 00123 -00131 00105 00151 00137 00165 00141 00150 00118 00053

Sep 17 -00084 00044 -00022 -00059 -00051 -00045 -00040 -00059 -00034

Okt 17 00063 -00083 00058 00011 00042 00012 00035 00007 00033

Nov 17 00016 00028 -00001 -00012 00004 00019 00036 -00006 00004 00077

Dez 17 00073 -00074 00050 00069 00062 00094 00089 00068 00056

Jan 18 -00170 00158 -00118 -00174 -00169 -00152 -00127 -00174 -00127

Feb 18 -00121 00185 -00213 -00179 -00193 -00191 -00169 -00182 -00201 -00208

Mrz 18 00175 -00141 00120 00078 00125 00167 00151 00077 00102

Apr 18 -00040 -00041 00076 00064 00024 00060 00059 00057 00085

Mai 18 00028 00016 00073 00054 -00009 00038 00037 00049 00090 00064

Jun 18 -00032 00027 00006 -00030 -00015 00008 00007 -00028 00004

Jul 18 -00134 00133 -00123 -00103 -00133 -00106 -00117 -00106 -00108

Aug 18 00021 00032 -00031 -00028 -00032 -00005 -00011 -00029 -00025 -00150

Sep 18 -00036 00035 -00057 -00073 -00049 -00005 -00016 -00074 -00061

Okt 18 -00093 00114 -00119 -00143 -00124 -00077 -00107 -00144 -00126

Nov 18 -00161 00207 -00167 -00248 -00198 -00179 -00206 -00242 -00189 -00267

Dez 18 -00018 00096 -00077 -00144 -00081 -00102 -00097 -00133 -00093

C8


C9


Jan 15 00221 00231 00216 00265 00208 00348

Feb 15 -00062 00012 -00047 00060 -00063 -00030 00214 00020

Mrz 15 00000 00033 -00008 00025 00014 -00107

Apr 15 -00060 -00014 -00068 00000 -00068 -00099

Mai 15 -00010 -00029 -00027 00039 -00018 00018 -00015 -00154

Jun 15 00030 -00020 00032 00020 00012 00002

Jul 15 -00150 -00138 -00150 -00207 -00148 00027

Aug 15 00247 00208 00239 00215 00247 00164 -00028 -00028

Sep 15 -00029 -00018 -00040 00024 -00038 -00003

Okt 15 -00081 -00093 -00073 -00138 -00091 -00176

Nov 15 00079 00081 00074 00050 00088 -00047 -00011 -00011

Dez 15 -00080 -00064 -00081 -00037 -00087 -00028

Jan 16 -00211 -00277 -00202 -00230 -00210 -00280

Feb 16 00018 -00108 00018 -00099 00028 -00037 -00295 -00166

Mrz 16 00118 00141 00118 00008 00116 00111

Apr 16 -00069 -00032 -00034 -00020 -00070 00094

Mai 16 00197 00215 00203 00202 00234 00159 00136 00264

Jun 16 -00189 -00111 -00172 -00099 -00185 -00222

Jul 16 00144 00110 00137 00158 00167 00097

Aug 16 -00186 -00194 -00181 -00147 -00202 -00106 00007 00025

Sep 16 00013 00039 00033 -00078 00029 -00003

Okt 16 -00029 00036 -00010 00054 -00017 -00087

Nov 16 -00011 -00025 00029 00068 00009 00064 00068 00211

Dez 16 00176 00180 00190 00165 00213 00186

Jan 17 -00174 -00123 -00202 -00127 -00168 -00046

Feb 17 -00116 -00094 -00075 -00132 -00129 -00285 -00039 -00015

Mrz 17 00025 00061 00053 00074 00061 00061

Apr 17 -00096 -00042 -00085 -00014 -00075 -00001

Mai 17 -00052 -00021 -00041 -00008 -00036 -00008 00022 00051

Jun 17 00046 00052 00057 00101 00055 -00076

Jul 17 -00085 -00035 -00074 -00052 -00078 -00053

Aug 17 -00223 -00242 -00206 -00189 -00208 -00201 -00128 -00042

Sep 17 00076 00027 00109 00024 00098 00090

Okt 17 00201 00252 00182 00208 00222 00248

Nov 17 -00445 -00374 -00440 -00355 -00467 -00338 -00043 -00043

Dez 17 00049 -00067 00068 -00001 00085 -00128

Jan 18 00032 00069 00066 00007 00023 00025

Feb 18 00188 00148 00204 00224 00219 00280 -00031 00105

Mrz 18 -00131 -00148 -00214 -00147 -00127 -00023

Apr 18 00084 00013 00131 -00016 00032 -00027

Mai 18 -00182 -00179 -00135 -00226 -00154 -00243 -00154 00047

Jun 18 00052 00005 00032 -00008 00117 -00016

Jul 18 00191 00178 00208 00186 00157 00075

Aug 18 -00035 00011 -00047 -00026 -00027 00008 00114 00199

Sep 18 00006 -00004 00001 00013 00009 00055

Okt 18 00067 -00002 00120 00068 00059 00097

Nov 18 00119 00090 00066 00041 00165 00196 -00015 00207

Dez 18 -00060 -00092 -00039 -00092 -00112 00008



11 Truck toll data














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EXTENDED USAGE OF ADMINISTRATIVE DATA FOR ......An analysis of cyclical trend movements, however,...

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