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Using SAS to implement additional tools

Using SAS to implement additional tools. 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

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Using SAS to implement additional tools

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  1. Using SAS to implement additional tools

  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. 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. 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. Checking quality • Grades (numerical values) are computed by SAS programs.

  6. 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)

  7. 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 :

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

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

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

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

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

  13. 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 ?

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