  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.
