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Identification of multiregime periodic autotregressive models by genetic algorithms

Author

Listed:
  • Domenico Cucina

    (UNISA - Università degli Studi di Salerno = University of Salerno)

  • Manuel Rizzo

    (UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome])

  • Eugen Ursu

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper develops a procedure for identifying multiregimePeriodic AutoRegressive (PAR) models. In each regime a possibly dif-ferent PAR model is built, for which changes can be due to the seasonalmeans, the autocorrelation structure or the variances. Number and lo-cations of changepoints which subdivide the time span are detected bymeans of Genetic Algorithms (GAs), that optimize an identification cri-terion. The method is evaluated by means of simulation studies, and isthen employed to analyze shrimp fishery data.

Suggested Citation

  • Domenico Cucina & Manuel Rizzo & Eugen Ursu, 2018. "Identification of multiregime periodic autotregressive models by genetic algorithms," Post-Print hal-03187870, HAL.
    • Handle: RePEc:hal:journl:hal-03187870
    Note: View the original document on HAL open archive server: https://hal.science/hal-03187870
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    References listed on IDEAS

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