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Parametric estimation of hidden Markov models by least squares type estimation and deconvolution

Author

Listed:
  • Christophe Chesneau

    (Université de Caen; LMNO)

  • Salima El Kolei

    (CREST;ENSAI)

  • Fabien Navarro

    (CREST; ENSAI)

Abstract

In this paper, we study a speci?c hidden Markov chain de?ned by the equation: Yi = Xi + ei, i = 1,...,n + 1, where (Xi)i=1 is a real-valued stationary Markov chain and (ei)i=1 is a noise independent of (Xi)i=1. We develop a new parametric approach obtained by minimization of a particular contrast taking advantage of the regressive problem and based on deconvolution strategy. We provide theoretical guarantees on the performance of the resulting estimator; its consistency and its asymptotic normality are established.

Suggested Citation

  • Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2017. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Working Papers 2017-66, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2017-66
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Contrast function; deconvolution; least square estimation; parametric inference; stochastic volatility;
    All these keywords.

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