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Testing Seasonal Adjustment with Demetra+

Testing Seasonal Adjustment with Demetra+. Ariunbold Shagdar National Statistical Office, Mongolia. The original series, real GDP by 2005 price. We used the data of GDP (quarterly nominal and real GDP at 2005 constant price). accuracy, From 2000 to 2011, by quarter

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Testing Seasonal Adjustment with Demetra+

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  1. Testing Seasonal Adjustment with Demetra+ Ariunbold Shagdar National Statistical Office, Mongolia

  2. The original series, real GDP by 2005 price We used the data of GDP (quarterly nominal and real GDP at 2005 constant price). accuracy, From 2000 to 2011, by quarter quality of production methods, consistency of time series issues to be improved in the future

  3. Real GDP Yes, seasonality is

  4. Describe the chosen approach and regressors I used TRAMO/SEATS Didn’t choose some predefined holidays or national holidays I was selected automatic specification(RSA1)

  5. Models applied Give information about pre-processing: the estimation time span used, (if) applied corrections for trading days and Easter, type of applied ARIMA model (p,d,q)(P,D,Q)s the dates and types of outliers as well as distribution of residuals

  6. Graph of the results The seasonal component is almost lost in the irregular.

  7. Check for moving seasonality Forth quarter where the moving seasonality is quite evident.

  8. Main Quality Diagnostics The result of the test is good (except the spectral td peaks/spectral analysis and regarima residuals/)

  9. Diagnostics summary was good. The definition(0.000) and annual totals(0.008) were very close to zero. In series, there may not be peaks at seasonal or trading day frequencies. visual spectral analysis spectral seas peaks: Good spectral td peaks: Bad

  10. The result of the remained test was good. residual seasonality on sa: Good (0.734) on sa (last 3 years): Good (0.983) on irregular: Good (0.638) outliers number of outliers: Good (0.023) seats seas variance: Good (0.779) irregular variance: Good (0.534) seas/irr cross-correlation: Good (0.419)

  11. Residual seasonality There is some residual seasonality after adjustment.

  12. Stability of model The revision dots are to the red line not much closer, the model is relative stable after the adjustment.

  13. Residuals Residuals almost follow the normal distribution. But we have some problems. They are random.

  14. Assess possibilities to publish the results I think so, it is possible to publish, but we need improve in the future.

  15. Conclusions Training course on seasonal adjustment would be an important tool for improving the quality of statistics by fostering the exchange of good practices.

  16. Things to consider in the future • How improve the seasonally adjusted data? • Trading day and Holiday effect. • Problems to solve - Didn’t specified the calendars. - Can’t distinguished the regressors. - If have the bad results then how adjust the time series? • Questions to the trainers in workshop II

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