STS-107 COLUMBIA ACCIDENT STS-107 COLUMBIA ACCIDENT MISSION,CREW.
Selective Editing Techniques and Seasonal Adjustment of STS
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Antonia Bertin, Marie Cordier- Villoing INSEE – STS department 28/04/201 4 Selective Editing Techniques and Seasonal Adjustment of STS
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Selective Editing Techniques and Seasonal Adjustment of STS. Outline. Short presentation on the use of selective editing techniques in monthly production of short-term economic indicators Three main points : General Process of selective-editing for STS (time series) - PowerPoint PPT Presentation
Transcript of Selective Editing Techniques and Seasonal Adjustment of STS
Antonia Bertin, Marie Cordier-VilloingINSEE – STS department
28/04/2014
Selective Editing Techniques and Seasonal Adjustment of STS
Outline
• Short presentation on the use of selective editing techniques in monthly production of short-term economic indicators
• Three main points :1. General Process of selective-editing for STS (time
series)2. Score function for Seasonnal Adjustement Quality
measures3. Next steps : improve the process
28/04/2014
Elaboration of a Short Term Indicator
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t+40
Contributions to GROWTH RATE Seasonal Adjustment QUALITY MEASURES
SELECTIVE EDITING ON RAW DATA
t+30Contributions to the GROWTH RATE
EDIT AND IMPUTATION
t+1Micro-editing on Business answers
(raw data, big outliers, missing values)
PUBLICATIONOutput : Time Series (indexes), and Growth Rates
SELECTIVE EDITING ON SEASONAL ADJUSTMENT
Demetra +X12 - Arima
Score function for SA Quality Measures
• The score function is a combination of various criteria : · Number and concentration of outliers;· Statistical properties of the Reg-ARIMA residuals ;· Lack of residual seasonality and trading-day effects in the
irregular component and in the seasonally adjusted series;· Similar to M and Q-statistics (Statistics Canada);· Revision analysis (stability of the model, stability of the
components).
• The global score is simple (-1,0,+1). The component scores show where the problem is.
• The “alarm matrix”28/04/2014
An example of “alarm matrix”
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Comparing the adjustments (1/2)
Comparing the adjustments (2/2)
Next steps
• Improving the imputation of missing data using the dynamic of the business answers.– For the big companies which tend to be late, – => forecast their answer using an ARIMA modeling of their
past answers, – possibly benchmarked on the estimated growth rate of the
sector.
• Improving the score function used to check the quality of the seasonal adjustment. – A principal component analysis could for example give a
better set of criteria to check and a better linear combination of them.
28/04/2014
Thanks for your attention!
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