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A Nonparametric Simulated Maximum Likelihood Estimation Method

Jean-David Fermanian and Bernard Salanié
Econometric Theory
Vol. 20, No. 4 (Aug., 2004), pp. 701-734
Stable URL: http://www.jstor.org/stable/3533543
Page Count: 34
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A Nonparametric Simulated Maximum Likelihood Estimation Method
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Abstract

Existing simulation-based estimation methods are either general purpose but asymptotically inefficient or asymptotically efficient but only suitable for restricted classes of models. This paper studies a simulated maximum likelihood method that rests on estimating the likelihood nonparametrically on a simulated sample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient for static models. We then propose an extension to dynamic models and give some Monte-Carlo simulation results on a dynamic Tobit model.

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