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An application of the Tramo Seats automatic procedure; direct versus indirect adjustment

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  • Agustín Maravall

    () (Banco de España)

Abstract

The ARIMA model based methodology of programs TRAMO and SEATS for seasonal adjustment and trend cycle estimation was applied to the exports, imports, and balance of trade Japanese series in Maravall (2002). The programs were used in an automatic mode, and the results analyzed. The present paper contains an extension of the work. First, some improvements in the automatic modelling procedure are illustrated, and the models for the seasonally adjusted series and its trend cycle component are discussed (in particular, their order of integration). It is further shown how the SEATS output can be of help in model selection. Finally, the important problem of the choice between direct and indirect adjustment of an aggregate is addressed. It is concluded that, because aggregation has a strong effect on the spectral shape of the series, and because seasonal adjustment is a non linear transformation of the original series, direct adjustment is preferable, even at the cost of destroying identities between the original series.

Suggested Citation

  • Agustín Maravall, 2005. "An application of the Tramo Seats automatic procedure; direct versus indirect adjustment," Working Papers 0524, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:0524
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    Cited by:

    1. Bujosa, Marcos & Garcia-Ferrer, Antonio & Young, Peter C., 2007. "Linear dynamic harmonic regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 999-1024, October.
    2. Alain Hecq & Sean Telg & Lenard Lieb, 2017. "Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?," Econometrics, MDPI, Open Access Journal, vol. 5(4), pages 1-22, October.
    3. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    4. Ball, V. Eldon & San Juan Mesonada, Carlos & Ulloa, Camilo A., 2011. "Agricultural productivity in the United States: catching-up and the business cycle," UC3M Working papers. Economics we1116, Universidad Carlos III de Madrid. Departamento de Economía.
    5. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2008. "Forecasting binary longitudinal data by a functional PC-ARIMA model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3187-3197, February.
    6. Carmen Maria Angyal, 2012. "The Study of Correlation between Stock Market Dynamics and Real Economy," EuroEconomica, Danubius University of Galati, issue 2(31), pages 14-22, May.
    7. Thornton, Michael A., 2013. "Removing seasonality under a changing regime: Filtering new car sales," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 4-14.
    8. Maravall, A. & del Rio, A., 2007. "Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 975-998, October.
    9. Fornaro, Paolo & Luomaranta, Henri, 2015. "Small Versus Large Firms Employment Patterns in Finland: a Comparison," MPRA Paper 66979, University Library of Munich, Germany.
    10. Phillips, Keith R. & Wang, Jack, 2016. "Residual seasonality in U.S. GDP data," Working Papers 1608, Federal Reserve Bank of Dallas.

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