Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities
研究了隐马尔可夫区制模型中最大似然估计的统计性质，允许协变量影响区制转移概率，并考虑了模型误设情况，通过蒙特卡洛模拟检验了有限样本表现。
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.