Identification of multiregime periodic autotregressive models by genetic algorithms

Domenico Cucina (), Manuel Rizzo () and Eugen Ursu ()
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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

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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.

Keywords: Seasonality; Structural changes; Genetic algorithm (search for similar items in EconPapers)
Date: 2018-09-19
Note: View the original document on HAL open archive server: https://hal.science/hal-03187870v1
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Published in International Conference of Time Series and Forecasting (ITISE 2018), Sep 2018, Grenade, Spain. pp.396-407

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