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

• 成果类型: Article
• 署名作者: Pouzo, Demian; Psaradakis, Zacharias; Sola, Martin
• 署名单位: University of California System; University of California Berkeley; University of London; Birkbeck University London; Universidad Torcuato Di Tella
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA17249
• 发表日期: 2022
• 页码: 1681-1710
• 关键词: AUTOREGRESSIVE MODELS; asymptotic properties; term structure; time-series; rates; Consistency; heteroskedasticity; normality; THEOREM; return

摘要

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.