REPUBLIC OF TURKEY TURKISH STATISTICAL INSTITUTE TurkStat Direct vs. Indirect Approach in Seasonal...

REPUBLIC OF TURKEY TURKISH STATISTICAL INSTITUTE TurkStat

Direct vs. Indirect Approach in Seasonal Adjustment: Proposal for a new tool

Necmettin Alpay KOÇAK

Akın ÖZTÜRK

Economic Indicators and Price Statistics Department

Information and Communication Technologies Department

2012 Workshop on recent advances in Seasonal Adjustment

6 March 2012, Luxembourg


Outline of the Presentation• Introduction• Motivation• New Tool : DISAT

– Hierarchy Tree– Aggregation– Analysis– Outputs

• Application and results (preliminary)• Conclusion and further steps


Introduction• When considering a single economic time series,

which method to be used has importance in seasonal adjustment,

• But, when a group of time series (i.e. balance of payments, national accounts, industrial production and sub-items) is of interest, the situation is slightly more complicated than previous. In this case, the discussion is on which approach (aggregated or disaggregated data) is to be used in seasonal adjustment.


Introduction

1. Direct approach

2. Indirect approach

• Since the choice between direct and indirect approach directly affects the information that is given to policy makers (Koçak, Mazzi and Moauro, 2010) the decision must be taken efficiently by agencies and organizations.


Introduction

• Extensive literature– Geweke (1978)– Ghysels, Granger and Siklos (1996)– Ghysels and Osborn (2001) – Hood and Findley (2001)– Astolfi, et al. (2001)– Maravall (2006)


Motivation• Considering the statistical classifications (i.e. NACE,

ISIC, etc.) used in production of data, it is a difficult task to compare direct and indirect approaches for each aggregated series (for each level of classification).

• The motivation of this study is the lack of an aggregation module to provide the series according to indirect approach and lack of a tool to easily calculate the criteria proposed in literature to compare of these two approaches.

• Another objective of this study is to extend the criteria previously explained by the literature. In detail, the diagnostics are extended by taking account not only final series but also preliminary series, the forecast functions of derived components.


New Tool : DISAT• Direct & Indirect Seasonal Adjustment Tool

(DISAT) • DISAT performs aggregation of the series using

by the outputs of individually seasonally adjusted series. During the aggregation process, it uses a classification structure defined by user and weights used in the classification to obtain indirectly seasonal adjusted series. Then, it provides to users both graphical views and statistical criteria to compare the directly and indirectly adjusted series.


New Tool : DISAT• This tool is designed to analyze the Excel outputs

obtained from TRAMO&SEATS for Windows, hereafter TSW (Caporello and Maravall, 2004), and Demetra+ seasonal adjustment softwares.

• DISAT needs three basic pieces of information as well as output files of TSW or Demetra+. – frequency of group of the time series (monthly and

quarterly)– the number of forecasts that are in the output files– classification system (hierarchy tree)


DISAT : Hierarchy tree

• The user must identify a hierarchical relationship between the series so that DISAT can perform aggregation process.

• Such classifications, NACE, MIGS, national accounts by production method, may be examples of this relationship


DISAT : Hierarchy tree• The series which is hierarchically at the top of the

group will be at the top of the hierarchy tree. During the creation process of that tree, the most important issue is weighting and it is possible to give weight by the user for each series in the process.

• NACE Rev.2 → hard process• Once the user created hierarchy tree, it is possible

to save this tree as an XML file and to use in other applications, subsequently.

• Using original series → no seasonality


DISAT : Aggregation• i = 1,2,...,n shows the number of the series K in the group, “O”

means that original series, A is aggregated one;

• Discripancies of below components will be tested for;– Linearized series– Trend-Cycle component– Irregular component (just to test residual seasonality)– Seasonal and calendar adjusted series– Seasonal adjusted series– Calendar adjusted series

OnnOOOt KbKbKbA ;;22;11; ...


DISAT : Aggregation

• The components here are obtained as level value of the series in case of additive decomposition, but in case of multiplicative decomposition, the components are obtained as factors.

x = S, C, I ttLogtLin Kxx ,1,, 100


DISAT : Analysis• Astolfi et al. (2001) Concordance ratio (ECB, 2010)

0 < Concordance ≤ 0.6→ No concordance

0.6 < Concordance ≤ 0.7→ Poor

0.7 < Concordance ≤ 0.8→Acceptable (Fair)

0.8 < Concordance ≤ 0.9→ Excellent (Good)

0.9 < Concordance ≤ 1→ Outstanding

n

GGD

n

tIndirecttDirectt

,,

1

1

12 1

2,,

n

DGGD

n

tIndirecttDirectt

IndirecttDirectt GGD ,,max3

IndirecttDirectt GGD ,,min4


DISAT : Analysis• Difference between

final estimator and preliminary estimators

Last three years Full sample Forecasts

• Dagum (1979)

N

ttt AA

NR

2

211 1

1

• Residual seasonality → Friedman Test on Irregular

Maravall (2007)


DISAT : Output• Group of the series

and growth rates of them– Linearized– Trend-Cycle– Seasonal adjusted– Calendar adjusted– Seasonal and calendar

adjusted

• Their graphics

• And diagnostics ; – Astolfi et al. (2001)– Concordance ratio– Last three years, full

sample and forecasts– Dagum (1979)– Residual seasonality

test


Application and results (preliminary)

GDP and sub-items according to production methods•21 time series•1988-Q1 and 2009-Q4

Seasonally adjusted with RSA=3 and IREG=1 by TSW

Gross Domestic Product ()A. Sectoral total1. Agriculture, hunting and forestry2. Fishing3. Mining and quarrying4. Manufacturing5. Electricity, gas and water supply6. Construction7. Wholesale and retail trade8. Hotels and Restaurants9. Transport, storage and communication10. Financial intermediation11. Ownership and dwelling12. Real estate, renting and business activities13. Public administration and defense; compulsory social security 14. Education15. Health and social work16. Other community, social and personal service activities17. Private household with employed personsB. Financial intermediation services indirectly measured (-) (FISIM)C. Taxes-Subsidies



Sectoral Total

GDP


A. Sectoral total

Last Three Years

Full sample Forecasts

D1 0.576 0.821 0.271

D2 0.476 0.592 0.139

D3 1.753 2.343 0.429

D4 0.001 0.001 0.063

Concordance Outstanding Outstanding Outstanding

A. Sectoral total

Measure of Roughness

Direct Adjustment

Indirect Adjustment

Last three years

726775 712188

Full sample 522633 531549

GDP

Last Three Years

Full sample Forecasts

D1 0.127 0.311 0.373

D2 0.101 0.303 0.056

D3 0.310 1.373 0.492

D4 0.026 0.009 0.299

Concordance Outstanding Outstanding Outstanding

GDP

Measure of Roughness

Direct Adjustment

Indirect Adjustment

Last three years

826489 836654

Full sample 586265 585273

Residual Seasonality Direct Indirect

A. Sectoral total 1.14 (0.999) 1.90 (0.996)GDP 1.58 (0.998) 1.58 (0.998)


Conclusion and further steps

• Development stage of DISAT will continue …

• DISAT tool will be a more effective tool when it contains other benchmarking criteria (revisions, sliding spans etc.) explained in the literature.

• This version has written in C#, next step is to transform it to Java and to provide possible implementation to JDemetra

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