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.
翻译:本文件考虑在隐藏的马尔科夫制度下,在一大批模型中进行最大可能性(ML)估计。我们调查了在一般条件下模型的ML估计值和当地无症状常态的一致性,这些模型允许在可观测进程中出现自动递减动态,Markov制度序列与共变量依赖的过渡矩阵,以及可能存在的模型误差。蒙特卡洛研究考察了ML估计值在正确指定和错误指定模型中的有限抽样特性。还讨论了经验应用。