Transcript P7.2.4

Composite Estimators,
Data Related Issues
(chapter 3)
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Frank van de Pol,
Jan van den Brakel,
Jan de Haan,
Pim Ouwehand,
Julian Chow
Proposed paragraphs
• Discontinuities in time series, back casting
• Unavailability of appropriate price indices
• Seasonal Adjustment: Problems related to the presence of
outliers and of seasonal and calendar effects
• Imputation of missing data ?
• Seasonal adjustment, comparison of X-12ARIMA with
TRAMO-SEATS ?
• Lack of information at desired frequency: Multi-frequency
models (MIDAS, Bridge Equations models, etc.)
Data Related Issues with Composite Estimators
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Discontinuities in time series, back casting
• Discontinuities due to changes in the real
world: intervention analysis
• Eliminate discontinuities due to survey
redesigns or new administrative legislation
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Double data collection, old and new way
Double data processing, old and new way
Intervention parameter in time series model
Once discontinuity is quantified, make old and
new data consistent
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Unavailability of appropriate price indices
• Deflation may be hampered by
• Across country differences
• In the definition of the price index
• In the timeliness of price indices
• Within country problems
• No recent price index survey available
• Foreign trade prices as substitute?
Data Related Issues with Composite Estimators
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Seasonal Adjustment: outliers and seasonal
and calendar effects
• When should an outlier be viewed as indicative of
a discontinuity, a crisis or a boom?
• Treat is as an outlier until the contrary is proven
• Statistical proof (standard error)
• Context proof (news item, change in related series)
• Should an outlier affect seasonal pattern or not?
• Correct treatment of calendar effects (holidays)
Data Related Issues with Composite Estimators
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Imputation of missing data ?
• Do missing data actually occur in the
highly aggregated time series that we
focus on in this handbook?
• No recent figures for some countries?
Data Related Issues with Composite Estimators
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Seasonal adjustment, comparison of X12ARIMA with TRAMO-SEATS
• History
• X12-ARIMA
• TRAMO-SEATS
• State space time series modelling,
Kalman filter
• Professional literature provides
comparisons
Data Related Issues with Composite Estimators
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Lack of information at desired frequency: Multifrequency models (MIDAS, Bridge Equations
models, etc.)
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Smoothing method (Ch.6 Handbook of Quarterly National Account,
Eurostat )
Two-step adjustment methods (Denton 1971, Ch.6 Handbook of
Quarterly National Account, Eurostat)
ARIMA approach (Wei and Stram 1990 and Al-Osh 1989, Ch.6 Handbook
of Quarterly National Account, Eurostat)
Optimal methods (Bournay and Laroque 1979, Fernàndez, 1981,
Litterman, 1983, Di Fonzo, 1987, Ch.6 Handbook of Quarterly National
Account, Eurostat)
Dynamic models (Ch.6 Handbook of Quarterly National Account, Eurostat)
Multivariate approach (Ch.11 Handbook of Quarterly National Account,
Eurostat)
State Space approach (Ruth 2008; S.J. Koopman, F. Palm, P.H. Franses)
Mixed Data Sampling (MIDAS) regression methods. (Ghysels, Sinko
and Valkanov 2006)
Bridge Equations models (Angelini, et al. 2008)
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