EconPapers    
Economics at your fingertips  
 

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

Demian Pouzo, Zacharias Psaradakis and Martin Sola

Econometrica, 2022, vol. 90, issue 4, 1681-1710

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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://doi.org/10.3982/ECTA17249

Related works:
Working Paper: Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities (2021) Downloads
Working Paper: Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities (2021) Downloads
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:wly:emetrp:v:90:y:2022:i:4:p:1681-1710

Ordering information: This journal article can be ordered from
https://www.economet ... ordering-back-issues

Access Statistics for this article

Econometrica is currently edited by Guido W. Imbens

More articles in Econometrica from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2025-03-30
Handle: RePEc:wly:emetrp:v:90:y:2022:i:4:p:1681-1710