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

Demian Pouzo (), Zacharias Psaradakis and Martin Sola

Department of Economics Working Papers from Universidad Torcuato Di Tella

Abstract: 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.

Keywords: Autoregressive model; consistency; covariatedependent transition probabilities; hidden Markov model; Markov-switching model; maximum likelihood; local asymptotic normality; misspecified models (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 (search for similar items in EconPapers)
Pages: 82 pages
Date: 2021-12
New Economics Papers: this item is included in nep-ore
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https://www.utdt.edu/download.php?fname=_164330670719540100.pdf (application/pdf)

Related works:
Journal Article: Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities (2022)
Working Paper: Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:udt:wpecon:2021_07

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