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Maximum Likelihood Estimation in Possibly Misspecified Dynamic Models with Time-Inhomogeneous Markov Regimes

Demian Pouzo, Zacharias Psaradakis () and Martin Sola ()

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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.

New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2016-12, Revised 2018-05
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http://arxiv.org/pdf/1612.04932 Latest version (application/pdf)

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Working Paper: Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes (2016) Downloads
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