Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities

This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Mar...

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Bibliographic Details
Published in:Econometrica 2022-07, Vol.90 (4), p.1681-1710
Main Authors: Pouzo, Demian, Psaradakis, Zacharias, Sola, Martin
Format: Article
Language:English
Subjects:
Autoregressive models
consistency
covariate‐dependent transition probabilities
hidden Markov model
local asymptotic normality
Markov‐switching model
Matrices
maximum likelihood
Maximum likelihood method
misspecified models
Normality
Probability
Sequences
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Summary:This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate‐dependent transition matrices, and possible model misspecification. A Monte Carlo study examines the finite‐sample properties of the ML estimator in correctly specified and misspecified models. An empirical application is also discussed.
ISSN:0012-9682
1468-0262
DOI:10.3982/ECTA17249