The recent economic crisis has altered the dynamics of economic series and, as a consequence, introduced uncertainty in seasonal adjustment of recent years. This problem was discussed in recent workshops at the European Central Bank and at Eurostat in the context of adjustment of the Euro Area Industrial Production (EPI) series.

Because a seasonal component is unobserved and undefined, it is difficult to compare results from different adjustment methods. Within the regARIMA model-based approach, however, a framework for systematic analysis is indeed present. The EPI series is analyzed under the TRAMO-SEATS framework. The purpose of the analysis is not to compare alternative methods, but to show how the results of the model-based analysis can be exploited at the identification, diagnostics and inference stages of modelling.

Despite the uncertainty induced by the crisis (and the revisions to the unadjusted data), the automatic procedure, with ramps to capture the spectacular 2008 drop in the series, provides excellent and stable results.

Keywords: Regression-ARIMA models; Automatic model identification; TRAMO-SEATS; X12-ARIMA

Biography: Ph.D. in Economics (U. of Wisconsin), Ph.D. in Engineering (U. of Madrid).

Chief Economist, Bank of Spain.

Formerly Full Professor, European University Institute, Florence; Economist, Federal Reserve Board of Governors, Washington, DC; Engineer, Ministry of Agriculture, Madrid.

Fellow ASA and Julius Shiskin Award for Economic Statistics, Washington Statistical Society, National Association of Business Economists, American Statistical Association. Fellow Journal of Econometrics. Jaime I Prize in Economics, Spanish Royal House and Generalitat de Valencia.