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Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

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Author Info

  • Dennis Kristensen
  • Yongseok Shin

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

We propose a simulated maximum likelihood estimator for dynamic models based on non- parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the like- lihood function. Using the simulated observations, we nonparametrically estimate the density - which is unknown in closed form - by kernel methods, and then construct a likelihood func- tion that can be maximized. We prove for dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. NPSML is applicable to general classes of models and is easy to implement in practice.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2008-58.

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Length: 45
Date of creation: 13 Nov 2008
Date of revision:
Handle: RePEc:aah:create:2008-58

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: dynamic models; estimation; kernel density estimation; maximum-likelihood; simulation;

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