1. Ketty Attal-Toubert and Stéphanie Himpens Insee 22nd of June, 2011 Using SAS to implement additional tools

2. Page 2 Summary of the cases in which SAS is used to implement additional tools › To identify and compute sets of regressors › To produce a detailed quality report › To compare quality reports, graphs and results of seasonal adjustments processed using different sets of specifications › To change specifications automatically when they lead to poor results › To see immediately what has been changed in the specifications of a workspace between month M and M+1

3. Page 3 Computing and identifying sets of regressors › We developped a specific way to compute our calendar regressors which allows us to test different kinds of hypotheses.

4. Page 4 Computing and identifying sets of regressors › Automatic choice is made between more than two sets of regressors. › Available sets depend on the sector of the series to process.

5. Page 5 Computing and identifying sets of regressors › No automatic choice of the most suitable set of regressors is available (demetra+ only tests if they’re significant or not). Output of the SAS programm :

6. Page 6 Checking quality › Grades (numerical values) are computed by SAS programs.

7. Page 7 Checking quality The different group of diagnoses are : › ARIMA modelling quality › Outlier identification (number of outliers, concentration) › Decomposition quality (residual effects, orthogonality of components) › History revisions (outlier stability)

8. Page 8 Checking quality › Weighted means of grades are computed to give us an idea of quality of the aggregates (indirect seasonal adjustment). Results are summed up in an Excel file :

9. Page 9 Comparing quality and results › Files are built to see which series show a decline or an improvement of its quality.

10. Page 10 Comparing quality and results › Files allow us to see immediately the changes in the specifications between two versions of a workspace. trend ARIMA models transformation Calendar regressors Number of outliers

11. Page 11 Comparing quality and results › Graphs overlaying results of different versions are drawed in SAS. It is possible to easily scroll the results.

12. Page 12 › It is easy to read and write specifications in SAS. Altering specifications Series’names Type of data file Location of data method Automatic ARIMA modelling is required The last columns are left in blank (ARIMA models are unfixed)

13. Page 13 Changing specifications automatically SAS allows us › to create mix models (for example old ARIMA models with new regressors). › To fix parts of models (the selected set of regressors for each series). › To change specifications in case of bad diagnostics (to test a model with the maximun number of calendar regressors every time residual calendar effects have been detected).

14. Page 14 Comparing two sets of specifications Are the two sets of specifications different or not ? Do they use the same data file ? Do they share the same options to find ARIMA models (initial specifications) ?

15. Page 15 Comparing two sets of specifications Two types of specifications can be compared : › It is possible to compare results : the models used to process the series with no care of whether they’re a result of the treatments or if they were fixed by the user ? › It is also possible to compare initial options : which process leads to this model ?