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Applying and interpreting model-based seasonal adjustment. The euro-area industrial production series

Author

Listed:
  • Agustín Maravall Herrero

    () (Banco de España)

  • Domingo Pérez Cañete

    (Banco de España)

Abstract

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 undefi ned, it is diffi cult to compare results from different adjustment methods. Within the regARIMA model-based approach, however, a framework for systematic analysis and comparison of results 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 identifi cation, diagnostics, and inference stages of modeling, and in the selection of an appropiate seasonal adjustment (and underlying model). 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.

Suggested Citation

  • Agustín Maravall Herrero & Domingo Pérez Cañete, 2011. "Applying and interpreting model-based seasonal adjustment. The euro-area industrial production series," Working Papers 1116, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:1116
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    File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/11/Fich/dt1116e.pdf
    File Function: First version, July 2011
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    References listed on IDEAS

    as
    1. Kaiser, Regina & Maravall, Agustin, 2005. "Combining filter design with model-based filtering (with an application to business-cycle estimation)," International Journal of Forecasting, Elsevier, vol. 21(4), pages 691-710.
    2. Víctor Gómez & Agustín Maravall, 1998. "Seasonal Adjustment and Signal Extraction in Economic Time Series," Working Papers 9809, Banco de España;Working Papers Homepage.
    3. Javier Andrés & Óscar Arce & Carlos Thomas, 2013. "Banking Competition, Collateral Constraints, and Optimal Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(s2), pages 87-125, December.
    4. McElroy Tucker & Wildi Marc, 2010. "Signal Extraction Revision Variances as a Goodness-of-Fit Measure," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-32, June.
    5. Maravall, Agustin, 1987. "Minimum Mean Squared Error Estimation of the Noise in Unobserved Component Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 115-120, January.
    6. McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(04), pages 988-1009, August.
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    Cited by:

    1. Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Efectos calendario sobre la producción industrial en Colombia," BORRADORES DE ECONOMIA 011241, BANCO DE LA REPÚBLICA.

    More about this item

    Keywords

    Time series analysis; Seasonal adjustment; Regression-ARIMA models; Filtering and smoothing; program TSW;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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