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Ronald van der Stegen
Reducing statistical discrepancy between direct and indirect GDP
Introduction: direct and indirect
– Direct seasonal adjustment of GDP: • seasonal adjustment of GDP
– Indirect seasonal adjustment of GDP:• sum of seasonally adjusted components of GDP
– 2013 first quarter:• Indirect GDP q-to-q growth -0.4%• Direct GDP q-to-q growth +0.1%
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Direct and indirect GDP
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Introduction: GDP
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Before SA
AfterSA
Gross Domestic product (GDP) S≠0 S’≈0
+Import S≠0 S’≈0
-Export S≠0 S’≈0
-Consumption of households S≠0 S’≈0
-Consumption of government S≠0 S’≈0
-Gross fixed capital formation S≠0 S’≈0
-Changes in stocks S≠0 S’≈0
=Statistical discrepancy (SD) from index formula (≠0: constant prices)
S≠0 S’>S
Project:
– Minimize: (SD(t)-SD(t-1))/GDP(t-1)
Achieved by:- Idea 1: Optimize X12-Arima- Idea 2: Multivariate pretreatment- Idea 3: Rebasing with multivariate Denton
Tested on data of 2013
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Quality of seasonally adjusted results
1. Standard quality measures of X12-Arima
2. Fluctuations in the statistical discrepancies
3. Revisions of published results
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Idea 1: improve settings of X12Arima
– Numerous settings tried:‐ Series are very volatile 2008 - today‐ Small reduction in fluctuation of SD possible by
harmonizing X12Arima setups
– Important sources for discrepancy are: ‐ Outliers‐ Regression effects‐ Extrapolation
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Idea 2: multivariate pretreatment
– Based on a structural time series model (STM)– Consistency constraints over
‐ Additive outliers‐ Level shifts‐ Time dependent regressors‐ Near future: time dependent seasonal factors
– STM removes above effects– Seasonal components of STM too volatile to use for seasonal
adjustment therefore seasonal adjustment by X11
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Idea 3: rebasing
– First idea 2 than Multivariate Denton technique
– All series are balanced to same order of magnitude
– Equal weights for the series
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Results: Quality measures X12Arima
– GDP: acceptable reduction in quality
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M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
M11
Q
Current
0.03
0.01
0.00
0.09
0.20
0.17
0.06
0.26
0.07
0.15
0.07
0.08
Idea 2
0.02
0.01
0.00
0.65
0.20
0.28
0.07
0.30
0.08
0.26
0.20
0.16
Idea 2+3
0.02
0.01
0.00
0.65
0.20
0.28
0.07
0.29
0.08
0.29
0.21
0.16
Results: Statistical discrepancy
– Significant reduction
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Results: Revisions
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– Similar revisions of the GDP
Conclusions
– More uniformity in seasonal adjustment results in less statistical discrepancy without significant reduction of the quality of the results
– Increased uniformity is established with multivariate pretreatment
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