Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators
Statistics Theory
2012-01-24 v2 Probability
Methodology
Statistics Theory
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 inverse of the Fisher-information matrix. 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.
Cite
@article{arxiv.1012.3851,
title = {Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators},
author = {Florian Gach and Benedikt M. Pötscher},
journal= {arXiv preprint arXiv:1012.3851},
year = {2012}
}
Comments
minor corrections, some discussion added, some material removed