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

    Pouzo, D. and Psaradakis, Zacharias and Sola, M. (2022) Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities. Econometrica , ISSN 0012-9682. (In Press)

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    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.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Autoregressive model, consistency, covariate-dependent transition probabilities, covariance matrix estimation, hidden Markov model, Markov-switching model, maximum likelihood, local asymptotic normality, misspecified models
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Zacharias Psaradakis
    Date Deposited: 10 Mar 2022 13:39
    Last Modified: 12 Mar 2022 07:14
    URI: https://eprints.bbk.ac.uk/id/eprint/47550

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