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, 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.

Automated summary

This paper investigates the consistency and local asymptotic normality of maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. The models consider 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, and an empirical application is also discussed.