direct vs. indirect approach in seasonal adjustment: proposal for a new tool
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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Outline of the Presentation• Introduction• Motivation• New Tool : DISAT
– Hierarchy Tree– Aggregation– Analysis– Outputs
• Application and results (preliminary)• Conclusion and further steps
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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.
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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.
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Introduction
• Extensive literature– Geweke (1978)– Ghysels, Granger and Siklos (1996)– Ghysels and Osborn (2001) – Hood and Findley (2001)– Astolfi, et al. (2001)– Maravall (2006)
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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.
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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.
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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)
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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
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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
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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; ...
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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
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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
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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)
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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
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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
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Application and results (preliminary)
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Application and results (preliminary)
Sectoral Total
GDP
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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)
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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