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Parametric estimation of hidden stochastic model by contrast minimization and deconvolution

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  • Salima El Kolei

Abstract

We study a new parametric approach for particular hidden stochastic models. This method is based on contrast minimization and deconvolution and can be applied, for example, for ecological and financial state space models. After proving consistency and asymptotic normality of the estimation leading to asymptotic confidence intervals, we provide a thorough numerical study, which compares most of the classical methods that are used in practice (Quasi-Maximum Likelihood estimator, Simulated Expectation Maximization Likelihood estimator and Bayesian estimators) to estimate the Stochastic Volatility model. We prove that our estimator clearly outperforms the Maximum Likelihood Estimator in term of computing time, but also most of the other methods. We also show that this contrast method is the most robust with respect to non Gaussianity of the error and also does not need any tuning parameter. Copyright Springer-Verlag Berlin Heidelberg 2013

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  • Salima El Kolei, 2013. "Parametric estimation of hidden stochastic model by contrast minimization and deconvolution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1031-1081, November.
    • Handle: RePEc:spr:metrik:v:76:y:2013:i:8:p:1031-1081
    DOI: 10.1007/s00184-013-0430-3
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    Cited by:

    1. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2022. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Statistical Papers, Springer, vol. 63(5), pages 1615-1648, October.
    2. El Kolei, Salima & Pelgrin, Florian, 2017. "Parametric inference of autoregressive heteroscedastic models with errors in variables," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 63-70.
    3. 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.

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