Anna Ciammola – ISTAT Meeting of the OECD Short-term Economic Statistics Working Party (STESWP)

1. Using results from revision analysis to improve compilation/estimation methods An application to the Italian IIP Anna Ciammola – ISTAT Meeting of the OECD Short-term Economic Statistics Working Party (STESWP)

2. Outline • Introduction • A case study: the Italian IIP Description of the approach  Presentation of the results

3. Introduction For users Objective  Availability of all the relevant information for using appropriately the estimates of ST indicators at different stages of the revision process provision of information about past revisions schedule future revisions (statistical and definitional) real-time databases gathering all the vintages analysis of size, bias and efficiency of revisions

4. Introduction • For producers • Underlying issues • Bias in the revision process • Inefficiency in compilation of preliminary estimates • Targets • Reduction of (the size of) “avoidable” revisions • Detection of the source for bias / inefficiency

5. A case study • Italian Index of Industrial Production (IIP) • Source and timing of revisions • Revision analysis • Top-down approach • Results

6. 1. Source and time of revisions

7. 2. Revision analysis IIP - Revisions on raw year-on-year growth rates Legend h=1 – after one month h=12 – after 12 months MAR –Mean Absolute Revision RMAR –Relative MAR MR –Mean Revision SD –Standard Deviation * a = 5%

8. 2. Revision analysis IIP - Revisions after one month on raw year-on-year growth rates

9. 3. Top-down approach • Tools • Revision measures ►Mean Revision ►Mean Absolute Revision ►Mean Squared Revisions (together with its decomposition) ►… • Weighted response rates • Average contribution of components to the MR of IIP index

10. 3. Top-down approach Diagram describing the top-down approach

11. 3. Top-down approach • Computation of the contribution to the MR • Revision of July 2004 and January months also affected by the revision of the productivity coefficients • Simulation exercise aimed at: • 1. highlighting the effect of the imputation of late respondents • 2. fulfilling the condition necessary to compute the average contribution of each components

12. 4. Results MIGS - Revisions after one month on raw Y-o-Y growth rates LegendCND – Consumer non durables CDU – Consumer durables CAP –Capital goods INT –Intermediate GoodsENE –Energy ° Period Jan-04 / Dec-07 *a = 5%

13. 4. Results Revisions after one month on raw Y-o-Y growth rates

14. 4. Results Average weighted response rates

15. 4. Results Revisions after one month on raw Y-o-Y growth rates Legend S–Selected subset of INT (19 NACE classes) NS –Complement of S in INT(S U NS = INT) * a = 5%

16. 4. Results Revisions after one month on raw Y-o-Y growth rates Legend S–Selected subset of INT (19 NACE classes) SC –Complement of S in IIP(S U SC = IIP) * a = 5%

17. 4. Results • Some evidences • Sectors in the subset S different in terms of either business concentration or production process (on order or not) • Reasons for revisions traced back to: ►partial information previously provided by respondents (especially small firms) and revised the month after ►estimation of the production levels of non respondents at the first release

18. 4. Results • Possible countermeasures • Intensive follow up of specific groups of units (especially for large firms that work on orders) • Different methods for the imputation of non responses ►some methodological proposals already implemented in the production process of IIP  taking into account firm size  several estimators

19. Acknowledgements • Teresa Gambuti – ISTAT IIP survey • Anna Rita Mancini – ISTAT IIP survey • Thank you!