A NONPARAMETRIC SIMULATED MAXIMUM LIKELIHOOD ESTIMATION METHOD

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...

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Bibliographic Details
Published in:Econometric theory 2004-08, Vol.20 (4), p.701-734
Main Authors: Fermanian, Jean-David, Salanié, Bernard
Format: Article
Language:English
Subjects:
Dynamic modeling
Econometrics
Economic models
Estimating techniques
Estimation
Estimation methods
Estimators
Infinity
Mathematical models
Maximum likelihood estimation
Maximum likelihood estimators
Maximum likelihood method
Modeling
Monte Carlo simulation
Parametric models
Probability
Simulations
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Summary: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.We thank Jean-Pierre Florens, Arnoldo Frigessi, Christian Gouriéroux, Jim Heckman, Guy Laroque, Oliver Linton, Nour Meddahi, Alain Monfort, Eric Renault, Christian Robert, Neil Shephard, and two referees for their comments. Remaining errors and imperfections are ours. Parts of this paper were written while Bernard Salanié was visiting the University of Chicago, which he thanks for its hospitality.
ISSN:0266-4666
1469-4360
DOI:10.1017/S0266466604204054