Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities
Demian Pouzo,
Zacharias Psaradakis and
Martin Sola
Papers from arXiv.org
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.
Date: 2016-12, Revised 2021-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://arxiv.org/pdf/1612.04932 Latest version (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) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1612.04932
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().