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

1. 2012 Workshop on recent advances in Seasonal Adjustment 6 March 2012, Luxembourg 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

2. Outline of the Presentation • Introduction • Motivation • New Tool : DISAT • Hierarchy Tree • Aggregation • Analysis • Outputs • Application and results (preliminary) • Conclusion and further steps

3. 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.

4. Introduction • Direct approach • 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.

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

6. 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.

7. 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.

8. 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)

9. 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

10. 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

11. 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

12. 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

13. 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 DISAT : Analysis

14. DISAT : Analysis • Difference between final estimator and preliminary estimators • Last three years • Full sample • Forecasts • Dagum (1979) • Residual seasonality → Friedman Test on Irregular Maravall (2007)

15. 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 DISAT : Output

16. 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

17. Application and results (preliminary)

19. Application and results (preliminary) Sectoral Total GDP

21. 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