Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators

Florian Gach and Benedikt Pötscher

MPRA Paper from University Library of Munich, Germany

Abstract: Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary non-parametric maximum likelihood density estimators are shown to be asymptotically normal. If the parametric model is correctly specified, it is furthermore shown that the asymptotic variance-covariance matrix equals the Cramér-Rao bound. These results are based on uniform-in-parameters convergence rates and a uniform-in-parameters Donsker-type theorem for non-parametric maximum likelihood density estimators.

Keywords: Indirect inference; simulation-based minimum distance estimation; non-parametric maximum likelihood; density estimation; efficiency (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 (search for similar items in EconPapers)
Date: 2010-12-16
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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