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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. V. Ball & Carlos San-Juan-Mesonada & Camilo Ulloa, 2014. "State productivity growth in agriculture: catching-up and the business cycle," Journal of Productivity Analysis, Springer, vol. 42(3), pages 327-338, December.
    2. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    3. 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.
    4. 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.
    5. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    6. 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.
    7. Fornaro, Paolo & Luomaranta, Henri, 2015. "Small Versus Large Firms Employment Patterns in Finland: a Comparison," MPRA Paper 66979, University Library of Munich, Germany.
    8. Ulloa, Camilo A. & Ball, V. Eldon & San Juan, Carlos, 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.
    9. Lisa Sella & Gianna Vivaldo & Andreas Groth & Michael Ghil, 2016. "Economic Cycles and Their Synchronization: A Comparison of Cyclic Modes in Three European Countries," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 25-48, September.
    10. 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.
    11. Tucker McElroy, 2018. "Seasonal adjustment subject to accounting constraints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 574-589, November.
    12. Daniel Thorburn & Can Tongur, 2014. "Assessing direct and indirect seasonal decomposition in state space," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 2075-2091, September.
    13. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
    14. 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.
    15. Keith R. Phillips & Jack Wang, 2016. "Residual seasonality in U.S. GDP data," Working Papers 1608, Federal Reserve Bank of Dallas.
    16. Michał Gradzewicz, 2019. "How do savings of different sectors respond to interest rate change?," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 1-22, March.
    17. 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.

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