1. April 2011 Testing Seasonal Adjustment with Demetra+ Ariunbold Shagdar National Statistical Office, Mongolia

2. Nov 2011 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 Nov 2011

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) Nov 2011

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 Nov 2011

6. Graph of the results The seasonal component is almost lost in the irregular. Nov 2011

7. Check for moving seasonality Forth quarter where the moving seasonality is quite evident. Nov 2011

8. Main Quality Diagnostics The result of the test is good (except the spectral td peaks/spectral analysis and regarima residuals/) Nov 2011

9. April 2011 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. April 2011 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. Nov 2011

12. Stability of model The revision dots are to the red line not much closer, the model is relative stable after the adjustment. Nov 2011

13. Residuals Residuals almost follow the normal distribution. But we have some problems. They are random. Nov 2011

14. Assess possibilities to publish the results I think so, it is possible to publish, but we need improve in the future. Nov 2011

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. Nov 2011

16. April 2011 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