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The Choice of Time Interval in Seasonal Adjustment: A Heuristic Approach

Author

Listed:
  • Giancarlo bruno

    (ISAE, Roma)

  • Edoardo Otranto

    (DEIR, Sassari)

Abstract

A typical problem of the seasonal adjustment procedures arises when the series to be adjusted is subject to structural breaks. In fact, using the full span of the series can result in a biased estimation of the ”true” seasonal adjusted series, with unclear evidence showed by the usual diagnostic tests. In these cases the researcher has to decide where to cut-o the observed series to obtain a homogeneous span; this is generally performed by a simple visual inspection studies of the graph of the series and/or using a-priori information about the occurrence of the break. In this paper we propose a statistical criterion based on a distance measure between filters, evaluating its performance with Monte Carlo experiments.

Suggested Citation

  • Giancarlo bruno & Edoardo Otranto, 2004. "The Choice of Time Interval in Seasonal Adjustment: A Heuristic Approach," Econometrics 0402008, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0402008
    Note: Type of Document - pdf; prepared on WinXP; to print on Laser witer II NP; pages: 14; figures: 4 figures in the document. PDF document submitted via ftp
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    References listed on IDEAS

    as
    1. Ghysels, Eric & Perron, Pierre, 1996. "The effect of linear filters on dynamic time series with structural change," Journal of Econometrics, Elsevier, vol. 70(1), pages 69-97, January.
    2. Ghysels, Eric & Perron, Pierre, 1993. "The effect of seasonal adjustment filters on tests for a unit root," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 57-98.
    3. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    4. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    5. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    6. Domenico Piccolo, 1990. "A Distance Measure For Classifying Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 153-164, March.
    7. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process? Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 396-397, July.
    8. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    9. Agustín Maravall, 1996. "Unobserved Components in Economic Time Series," Working Papers 9609, Banco de España.
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    Cited by:

    1. Bhattacharya, Rudrani & Pandey, Radhika & Patnaik, Ila & Shah, Ajay, 2016. "Seasonal adjustment of Indian macroeconomic time-series," Working Papers 16/160, National Institute of Public Finance and Policy.

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    More about this item

    Keywords

    Linear filters; Structural break; Distance.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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