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* Corresponding author 1 LM - Laboratoire de mathématiques de Brest

Abstract : Many nonlinear time series models have been proposed in the last decades. Among them, the models with regime switchings provide a class of versatile and interpretable models which have received a particular attention in the literature. In this paper, we consider a large family of such models which generalize the well known Markov-switching AutoRegressive MS-AR by allowing non-homogeneous switching and encompass Threshold AutoRegressive TAR models. We prove various theoretical results related to the stability of these models and the asymptotic properties of the Maximum Likelihood Estimates MLE. The ability of the model to catch complex nonlinearities is then illustrated on various time series.

Keywords : Markov-switching autoregressive process non-homogeneous hidden Markov process maximum likelihood consistency stability lynx data wind direction





Autor: Pierre Ailliot - Françoise Pene -

Fuente: https://hal.archives-ouvertes.fr/



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