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

Abstract

This paper considers maximum likelihood (ML) estimation in a largeclass of models with hidden Markov regimes. We investigate consistencyof the ML estimator and local asymptotic normality for the models undergeneral conditions which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate-dependent transitionmatrices, and possible model misspecification. A Monte Carlo study examines the finite-sample properties of the ML estimator in correctly specifiedand misspecified models. An empirical application is also discussed.