Post on 08-Jul-2020
The Euro Area Flash Estimation ProcedurePedro Martins Ferreira and Luca Gramaglia
Eurostat, European Commissionestat-hicp@ec.europa.eu
Problem statement• Eurostat has been publishing the euro area Harmonised Index of Consumer Prices (HICP) flash estimate (all-items) every month since October 2001 but its relevance has been somewhat limited to the users
as only the HICP all-items was released and no flash indication on its main components were made available;
• Inflation main components, i.e., ’food’, ’non-energy industrial goods’ (igoodsxe), ’energy’ and ’services’ (serv) are more difficult to forecast. In addition, the outcome of the nowcasting procedure has to be internally consistent, i.e., the aggregation of the nowcasts for the main components has to equal the nowcast of the all-items;
• A completely new nowcasting system had to be developed. The new system combines early information from the euro area Member States with 1-step ahead forecasts and timely price data on specific energy products into a consistent set of nowcasts.
ConclusionFrom October 2012 onwards Eurostat produced timely and accurate flash
estimates. The maximum recorded error was 0.2 percentage points for energy, the most volatile component, and no error for the headline inflation
(all-items) was recorded so far.
Automatic model selection1. For each parameter model combination, perform a pseudo-
out-of-sample forecast over the last 24 months (n = 24). Compute and record ME and MSE;
2. Sort the results by MSE in ascending order;
3. Test if the ME of the model of the ordered list is significantly different from zero using the t-test. If the ME is significantly different from zero, discard that model and test the next model from the sorted list. Repeat while t-test is rejected;
4. Perform the pseudo-out-of-sample for the last 24 months us- ing both the reference model and the candidate model and save the two vectors of forecasting errors;
5. Compare the forecast errors vectors using the Diebold-Mariano statistic. If the MSE of the forecast errors of the candidate model is significantly lower than that of the reference model, replace the reference model by the candidate model.
CalibrationThe calibration is made by a simple proportional allocation. Nevertheless, a new calibration procedure is being developed, which will be applied both to preliminary data and nowcasts.
� =
IAll items
t
aggregation
⇣IMain component
t
⌘
Figure 3: Calibration factor density plot. The calibration factor φ is the scalar that needs to be multiplied with the non-calibrated main components nowcasts to make them aggregate to the all-items nowcast. The closer φ is to 1, the less the main components nowcasts need to be changed to meet the consistency constraint.
Flash Estimate procedure
Results
Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13
All-itemsFE 2.51 2.25 2.23 2.02 1.84 1.75
All-items HICP 2.49 2.19 2.22 1.98 1.84 1.74All-items
Diff. 0.02 0.05 0.01 0.04 0.00 0.01
Food
FE 3.18 3.04 3.15 3.23 2.73 2.70
Food HICP 3.10 3.03 3.16 3.24 2.72 2.71Food
Diff. 0.08 0.01 -0.01 -0.01 0.01 -0.01
Non-energy industrial goods
FE 1.14 1.15 1.09 0.83 0.82 0.99Non-energy industrial goods
HICP 1.10 1.10 1.05 0.78 0.78 0.97
Non-energy industrial goods Diff. 0.04 0.05 0.05 0.05 0.04 0.02
Energy
FE 7.75 5.80 5.25 3.93 3.99 1.66
Energy HICP 7.96 5.72 5.23 3.90 3.92 1.67Energy
Diff. -0.21 0.08 0.01 0.04 0.07 -0.01
Services
FE 1.78 1.70 1.78 1.75 1.59 1.86
Services HICP 1.73 1.64 1.79 1.64 1.54 1.85Services
Diff. 0.04 0.06 -0.01 0.11 0.04 0.01
Table 2: Nowcast results. fe: flash estimate annual rate; hicp: final annual rate; diff.: difference in percentage points between the flash estimate and the final figure. igoodsxe: ’non-energy industrial goods’. Unrounded differences.
Acknowledgments: The authors would like to express their gratitude to Jarko Pasanen and Roberto Barcellan who gave valuable input through comments, ideas, suggestions, guidance or simply through their pertinent questions that made us think more deeply on the methodology being developed. The other part of the production process which involves, among other things, the publication material, would not have been possible without the participation of Svetoslava Pavlova and Sílvia Santos.
Disclaimer: The content of this poster does not reflect the official opinion of the Eurostat. Responsibility for the information and views expressed therein lies entirely with the authors.
Preliminary dataPreliminary data sent by Member States has a 7 to 10 times lower RMSE than 1-step ahead forecasts described in the literature, which corroborates our assumption that preliminary data is a better forecast than any model-based forecast.
Aggregate Period Coverage MAPE max RMSE Rel RMSE
All-itemsP1 95% 0.051 0.117 0.034 0.073
All-itemsP2 98% 0.053 0.089 0.028 0.060
food P2 85% 0.053 0.189 0.066 0.216
igoodsxe P2 85% 0.110 0.305 0.106 0.285
energy P2 85% 0.136 0.358 0.148 0.086
serv P2 85% 0.100 0.207 0.070 0.149
Table 1: Preliminary data accuracy. P1: from Jan-2011 to Feb-2012; P2: from Mar-2012 to Jan-2013; MAPE: weighted mean absolute percentage error; max: maximum absolute difference in percentage error; RMSE: root mean square error; Rel. RMSE: root mean square error relative to a benchmark model.
Time constraintTheoretical framework
Nt = ��1 (B)w (B)Xt +��1n (B) ✓n (B)
� (B)s � (Bs)�Ds �d
at
: aggregate of countries that didn’t provide preliminary data : set of regressors (energy prices and / or aggregate of countries which did provide preliminary data
Nt
Xt
Forecasting EnergyEnergy is the most volatile component...
−0.15
−0.10
−0.05
0.00
0.05
0.10
0.15
2007 2008 2009 2010 2011 2012 2013
all
food
igoodsxe
energy
serv
Figure 1: ∆12 log for the euro area HICP for the all-items and main components. Energy is clearly the most volatile main component.
Figure 2: HICP versus energy prices. Since COICOP 0722 is essentially a mix of ’diesel’ and ’petrol’, a linear combination of these two energy products was calculated as Ct = α · diesel + (1 − α) · petrol, where α was chosen in order to maximize the correlation between ∆logHICP and ∆logCt.
...but energy prices published weekly by DG ENER, European Commission are good regressors of the HICP ‘energy’ component.
Consistency constraint
!1.5%
!0.5%
0.5%
1.5%
2.5%
3.5%
4.5%
Jan!06%
Apr!06%
Jul!06%
Oct!06%
Jan!07%
Apr!07%
Jul!07%
Oct!07%
Jan!08%
Apr!08%
Jul!08%
Oct!08%
Jan!09%
Apr!09%
Jul!09%
Oct!09%
Jan!10%
Apr!10%
Jul!10%
Oct!10%
Jan!11%
Apr!11%
Jul!11%
Oct!11%
Jan!12%
Apr!12%
Jul!12%
Oct!12%
Jan!13%
food% igoodsxe% energy% serv% all%
Deeper insight into inflation...
...in a more timely manner
More information available
• Eurostat webpage• News release• Statistics explained
http://ec.europa.eu/eurostat
51/2013 - 3 April 2013
Flash estimate - March 2013
Euro area annual inflation down to 1.7%
Euro area1 annual inflation2 is expected to be 1.7% in March 2013, down from 1.8% in February3, according to a
flash estimate4 from Eurostat, the statistical office of the European Union.
Looking at the main components of euro area inflation, food, alcohol & tobacco is expected to have the highest
annual rate in March (2.7%, stable compared with February), followed by services (1.9% compared with 1.5% in
February), energy (1.7% compared with 3.9% in February) and non-energy industrial goods (1.0% compared with
0.8% in February).
Euro area annual inflation and its components, %
Weight (‰)
2013 Mar 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Mar 2013
All-items HICP 1000.0 2.7 2.5 2.2 2.2 2.0 1.8p 1.7e
Food, alcohol & tobacco 193.7 3.3 3.1 3.0 3.2 3.2 2.7p 2.7e
Energy 109.6 8.5 8.0 5.7 5.2 3.9 3.9p 1.7e
Non-energy industrial goods 273.6 1.4 1.1 1.1 1.0 0.8 0.8p 1.0e
Services 423.0 1.8 1.7 1.6 1.8 1.6 1.5p 1.9e
Source: Eurostat e = estimate p = provisional
1. The euro area consists of Belgium, Germany, Estonia, Ireland, Greece, Spain, France, Italy, Cyprus, Luxembourg, Malta,
the Netherlands, Austria, Portugal, Slovenia, Slovakia and Finland.
2. Annual inflation is the change of the price level between the current month and the same month of the previous year. For
further information on the euro area inflation flash estimate, see the Statistics Explained article on the Eurostat website:
http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Inflation_in_the_euro_area
3. See News Release 41/2013 of 15 March 2013.
4. The euro area inflation flash estimate is issued at the end of each reference month. The complete set of harmonised indices
of consumer prices (HICP) for the euro area, EU and Member States is released around the middle of the month following
the reference month. The next release with full data for March 2013 is scheduled for 16 April 2013.
Issued by Eurostat Press Office
Tim ALLEN
Tel: +352-4301-33 444
eurostat-pressoffice@ec.europa.eu
For further information on data:
Svetoslava PAVLOVA
Tel: +352-4301-34 406
estat-prc-stats-methods@ec.europa.eu
Eurostat News Releases on the internet: http://ec.europa.eu/eurostat
Selected Principal European Economic Indicators: http://ec.europa.eu/eurostat/euroindicators
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Table 1: Euro area annual in
flation and its main
components (%), 2
013, March
2012 and October 2
012
- March
2013
Source: E
urostat (p
rc_hicp
_manr)
(http://ec.e
uropa.eu/eurostat/product?
code=prc_hicp
_manr&language=en&mode=view)
Figure 1: Euro area annual in
flation and its main
components (%), 2
002-2013-03
Source: E
urostat (p
rc_hicp
_manr)
(http://ec.e
uropa.eu/eurostat/product?
code=prc_hicp
_manr&language=en&mode=view)
Figure 2: Euro area annual in
flation and its main
components (%), S
eptember 2010 - M
arch 2013
Source: E
urostat (p
rc_hicp
_manr)
(http://ec.e
uropa.eu/eurostat/product?
code=prc_hicp
_manr&language=en&mode=view)
Figure 3: Weights o
f the main co
mponents of th
e euro
area HICP (2013)
Source: E
urostat (p
rc_hicp
_inw)
(http://ec.e
uropa.eu/eurostat/product?
code=prc_hicp
_inw&language=en&mode=view)
Inflatio
n in th
e euro area
From Statistic
s Explained
Data from M
arch 2013. M
ost rece
nt data: Furth
er Eurosta
t inform
ation, Main tables
and Database.
The data
in this artic
le show the m
ost rece
nt annual r
ates o
f change fo
r the e
uro area h
eadline in
flation an
d its four m
ain co
mponents issu
ed by Eurostat. The fi
gures
presented
are ac
tual HICP fig
ures.
Contents
1 Main
statist
ical fi
ndings
1.1 Flash est
imate an
d full HICP data
1.2 Main
components o
f inflat
ion
2 Further Eurostat
information
2.1 Database
2.2 Dedicated
section
2.3 Meth
odology / Meta
data
3 See also
Main statistic
al findings
The euro are
a annual i
nflation rat
e was 1
.7% in Marc
h 2013, down from 1.8% in February
. A year
earlie
r the
rate was 2
.7%. The flash
estimate
for Marc
h, published on 3 April
2013, was c
onfirmed. Further i
nformation on
the accu
racy of th
e flash
estimate
s can be fo
und in the artic
le Inflat
ion – methodology of th
e euro are
a flash
estimate
.
Looking at the m
ain co
mponents of th
e euro are
a inflat
ion, 'Food, al
cohol & tobacc
o' (2.7%, st
able compare
d to
February) had the h
ighest annual r
ate in M
arch, fo
llowed by 'Service
s' (1.8%, up fro
m 1.5% in February),
'Energy' (1
.7%, down from 3.9% in February
) and 'N
on-energ
y industrial g
oods' (1.0%, up fro
m 0.8% in
February).
Flash estimate and fu
ll HICP data
The euro are
a inflat
ion flash est
imate is
issued at
the end of ea
ch refere
nce month or sh
ortly aft
er. When the
complete set
of harmonised
indices o
f consumer p
rices (H
ICP) is rel
eased aro
und the middle o
f each month
following the refere
nce month, es
timate
d data are
replac
ed by actual d
ata deriv
ed from the M
ember S
tates
figures. Further i
nformation on the a
ccurac
y of the fl
ash est
imates ca
n be found in the a
rticle I
nflation –
methodology of th
e euro are
a flash
estimate
.
As from Septem
ber 2012, th
e flash
estimate
releas
e inclu
des, in ad
dition to the h
eadline in
flation, es
timate
s of
its four m
ain co
mponents.
The main
components a
nd their rela
tive weig
hts for 2013 are
presented
in Figure 3 an
d Table 2, re
spectively
.
Main components of infla
tion
Each of th
e main
components c
ontributes
in varying degree
to the head
line inflat
ion in the euro are
a. In ter
ms
of weig
ht, set a
t 1000 for the a
ll-item
s HICP, se
rvices is
the largest
component, acco
unting for around 42.3% of
individual consumption ex
penditure in the e
uro area. I
t is followed by non-en
ergy industri
al goods w
ith around
27.4%.
Food, alcohol &
tobacco an
d energ
y account fo
r 19.4% and 11.0%, re
spectively
. Together,
they comprise
less
than one third of eu
ro area e
xpenditure, but th
ey can have si
gnificant im
pacts o
n the head
line inflat
ion as their
levels
tend to flu
ctuate
signific
antly more than the o
ther components.
The brea
kdown of the H
ICP into four main
components d
oes not fo
llow the standard
COICOP classif
ication,
but groups item
s from diffe
rent ex
penditure class
es into four broad product c
ategories
. For further d
etails
on the
composition/su
b-indices, p
lease s
ee Eurostat
's COICOP/HICP cla
ssifica
tion
(http://ec.e
uropa.eu/eu
rostat/ram
on/nomenclatures
/index.cfm?
TargetU
rl=LST_NOM_DTL&StrN
om=HICP_2000&StrLanguageCode=EN&IntPcK
ey=&StrLayoutCode=EN) .
Further E
urostat inform
ation
Database