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Ergodicity of Autoregressive Processes with Markov-Switching and Consistency of the Maximum-Likelihood Estimator 

Authors: Christian Francq a; Michel Roussignol b
Affiliations:   a Universiteacute de Lille I, France
b Universiteacute de Marne la Valleacutee, France
DOI: 10.1080/02331889808802659
Publication Frequency: 6 issues per year
Published in: journal Statistics, Volume 32, Issue 2 1998 , pages 151 - 173
Formats available: PDF (English)
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Abstract

An autoregressive model with Markov-switching assumes a sequence of random vectors to be a non linear autoregressive model given a sequence of non observed state variables which forms a Markov chain. A particular case of this model is the hidden Markov model. In this paper conditions for the existence of an ergodic stationary solution are given and consistency of the maximum likelihood estimator is proved.
Keywords: Non linear time series models; hidden Markov chain; switching models; maximum likelihood; consistency
AMS Subject Classification: Primary: 62M10; Secondary: 62M09
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