Ergodicity of Autoregressive Processes with Markov-Switching and Consistency of the Maximum-Likelihood Estimator
Authors:
Christian Francq a;
Michel Roussignol b
| Affiliations: | a Universit de Lille I, France |
b Universit de Marne la Vall e, France |
DOI:
10.1080/02331889808802659
Publication Frequency:
6 issues per year
Subjects:
Mathematical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Formats available:
PDF
(English)
View Article:
View Article (PDF)
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 |
| view references (13) |

Download Citation


de Lille I, France
CiteULike
Del.icio.us
BibSonomy
Connotea