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Econometrica Vol. 90 No. 4 2022

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

Demian Pouzo1; Zacharias Psaradakis2; Martin Sola3

1 Department of Economics, University of California, Berkeley · 2 Department of Economics, Mathematics and Statistics, Birkbeck, University of London · 3 Department of Economics, Universidad Torcuato Di Tella

Abstract

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.

DOI
10.3982/ecta17249
Volume
90
Issue
4
Pages
1681-1710
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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