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

Demian Pouzo (), Zacharias Psaradakis and Martin Sola

Department of Economics Working Papers from Universidad Torcuato Di Tella

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 misspeci cation. A Monte Carlo study examines the nite-sample properties of the ML estimator. An empirical application is also discussed. Key words and phrases: Autoregressive model; consistency; hidden Markov model; Markov regimes; maximum likelihood; local asymptotic normality; misspeci ed models; time-inhomogenous Markov chain.

Keywords: Autoregressive model; consistency; hidden Markov model; Markov regimes; maximum likelihood; local asymptotic normality; misspeci ed models; time-inhomogenous Markov chain. (search for similar items in EconPapers)
Pages: 53 pages
Date: 2016-12
New Economics Papers: this item is included in nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:udt:wpecon:2016_04

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