1. Revisions in raw, working-day and seasonally adjusted data
An application to the Italian industrial production index
Anna Ciammola
ISTAT

2. Layout Statement of the problem
Approaches to analyse revisions in seasonally adjusted (SA) data
The “empirical” approach
An application
Steswp meeting - Paris June 2007

3. Statement of the problem
Revisions of seasonally adjusted data can be traced back to different sources:
Revisions in raw data
Sources data, benchmarking, rebasement, classification, methodology, …
Revisions in working-day factors
Easter, working/trading day, leap-year, holidays
3. Revisions in seasonal factors
Concurrent adjustment, forecasted seasonal factors
Steswp meeting - Paris June 2007

4. Statement of the problem
A simple representation of the problem using the following notation
Ijsa,i
I refers to a specific economic indicator
j refers to the state of raw data 
j=p preliminary data
j=f final data
i refers to the state of seasonally adjusted data 
i=p preliminary seasonal factors
i=f final seasonal factors
Steswp meeting - Paris June 2007

5. Statement of the problem
Seasonal revisions
Revisions in raw data
Rsa R
RT total revision
Revisions in raw data
Seasonal revisions R
R’sa
Source: Maravall and Pierce, 1983
Steswp meeting - Paris June 2007

6. Statement of the problem
The revision processes R and Rsa
may be very different
The analysis of revisions only on SA
data is not sufficient to describe the statistical properties of revisions
Steswp meeting - Paris June 2007

7. Approaches “Analytical” approach Decomposition of the total revision
RT = f (R , Rsa)
Interesting, but not always applicable
Example: Row, WDA and SA Italian QNA derive from three different process of temporal disaggregation
Steswp meeting - Paris June 2007

8. Approaches “Empirical” approach Analysis of three sets of revisions:
- on raw data
- on WDA data
- on SA data
Less appealing, but:
- always applicable
- simple for dissemination purposes
Steswp meeting - Paris June 2007

9. The “empirical” approach
Raw data
WDA data
SA data
Revisions as:
Error
Relative error
Revisions on:
Levels
Y-o-Y changes
P-o-P changes
Indices of:
location
variability
systematic component
Steswp meeting - Paris June 2007

10. The “empirical” approach
Revisions in raw and WDA data
- Stability of WDA factors always checked in the decomposition processes
- RWDA = g (R , B)
(B changes in the parameter estimates of regressors in reg-ARIMA models)
- Changes in B should be small
Revisions process in WDA data
Revisions process in raw data
Steswp meeting - Paris June 2007

11. The “empirical” approach
Revisions in raw and SA data
- Stability of SA factors always checked in the decomposition processes
- Analysis of revisions on raw data is a helpful tool to understand the size and other properties of revisions on SA data
Example: Bias in revisions of SA data
 bias in revisions of raw data?
 bias due to the SA process?
Steswp meeting - Paris June 2007

12. An application
The revision of the Italian industrial production index (Intermediate goods)
Sources of revision
additional data arrived from late respondents (increase in coverage);
correction of errors in data already embodied in the estimates
revision of statistics (external to the survey) utilised in compiling the index (e.g. productivity coefficients drawn from national accounts)
Timing of revision (from October 2004)
First revision: 1 month after the preliminary release
Second revision at fixed points
- April (releasing February data and concerning the previous three years)
- October (releasing August data and concerning the first semester of current year)
Steswp meeting - Paris June 2007

13. An application
Steswp meeting - Paris June 2007

14. An application
Steswp meeting - Paris June 2007

15. An application
Steswp meeting - Paris June 2007

16. Thank you!