Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

Dennis Kristensen and Yongseok Shin

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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 likelihood function. Using the simulated observations, we nonparametrically estimate the density - which is unknown in closed form - by kernel methods, and then construct a likelihood function 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.

Keywords: dynamic models; estimation; kernel density estimation; maximum-likelihood; simulation (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 C32 C35 (search for similar items in EconPapers)
Pages: 45
Date: 2008-11-13
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (17)

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Journal Article: Estimation of dynamic models with nonparametric simulated maximum likelihood (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2008-58

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