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Revisions in raw, working-day and seasonally adjusted data. An application to the Italian industrial production index Anna Ciammola ISTAT. Layout. Statement of the problem Approaches to analyse revisions in seasonally adjusted (SA) data The “empirical” approach An application.
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Revisions in raw, working-day and seasonally adjusted data An application to the Italian industrial production index Anna Ciammola ISTAT
Layout • Statement of the problem • Approaches to analyse revisions in seasonally adjusted (SA) data • The “empirical” approach • An application
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
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
Statement of the problem Revisions in raw data Seasonal revisions Rsa R RT total revision Revisions in raw data Seasonal revisions R R’sa Source: Maravall and Pierce, 1983
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
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
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
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
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?
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)
