On recursive estimation for hidden Markov models
AbstractHidden Markov models (HMMs) have during the last decade become a widespread tool for modelling sequences of dependent random variables. In this paper we consider a recursive estimator for HMMs based on the m-dimensional distribution of the process and show that this estimator converges to the set of stationary points of the corresponding Kullback-Leibler information. We also investigate averaging in this recursive scheme and show that conditional on convergence to the true parameter, and provided m is chosen large enough, the averaged estimator is close to optimal.
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Bibliographic InfoArticle provided by Elsevier in its journal Stochastic Processes and their Applications.
Volume (Year): 66 (1997)
Issue (Month): 1 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description
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- Driffill, John & Kenc, Turalay & Sola, Martin & Spagnolo, Fabio, 2004.
"On Model Selection and Markov Switching: A Empirical Examination of Term Structure Models with Regime Shifts,"
CEPR Discussion Papers
4165, C.E.P.R. Discussion Papers.
- John Driffill & Turalay Kenc & Martin Sola & Fabio Spagnolo, 2008. "On Model Selection and Markov-Switching: An Empirical Examination of Term Structure Models with Regime Shifts," Department of Economics Working Papers 2008-04, Universidad Torcuato Di Tella.
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