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    Maximum likelihood estimation in possibly misspecified dynamic models with time-inhomogeneous Markov Regimes

    Pouzo, D. and Psaradakis, Zacharias and Sola, M. (2016) Maximum likelihood estimation in possibly misspecified dynamic models with time-inhomogeneous Markov Regimes. Working Paper. Birkbeck College, University of London, London, UK.

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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 and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspecification. A Monte Carlo study examines the finite-sample properties of the ML estimator. An empirical application is also discussed.

    Metadata

    Item Type: Monograph (Working Paper)
    Additional Information: ISSN 1745-8587: BWPEF 1607
    Keyword(s) / Subject(s): Autoregressive model, consistency, hidden Markov model, Markov regimes, maximum likelihood, local asymptotic normality, misspecified models, time-inhomogenous Markov chain
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Administrator
    Date Deposited: 04 Jul 2017 10:23
    Last Modified: 04 Jul 2017 10:39
    URI: http://eprints.bbk.ac.uk/id/eprint/19109

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