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Overcoming Nonadmissibility in ARIMA-Model-Based Signal Extraction

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  • Fiorentini, Gabriele
  • Planas, Christophe

Abstract

We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to draw a comparison with the results yielded by the AMB decomposition of an approximated model by using an ad hoc filter such as X11-ARIMA and by direct specification of the structural time series models.

Suggested Citation

  • Fiorentini, Gabriele & Planas, Christophe, 2001. "Overcoming Nonadmissibility in ARIMA-Model-Based Signal Extraction," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 455-464, October.
    • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:4:p:455-64
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    Cited by:

    1. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
    2. Guy Mélard, 2016. "On some remarks about SEATS signal extraction," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 53-98, March.

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