Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference
Statistics Theory
2011-01-10 v2 Statistics Theory
Abstract
Given a random sample from a parametric model, we show how indirect inference estimators based on appropriate nonparametric density estimators (i.e., simulation-based minimum distance estimators) can be constructed that, under mild assumptions, are asymptotically normal with variance-covarince matrix equal to the Cramer-Rao bound.
Cite
@article{arxiv.0908.0433,
title = {Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference},
author = {Richard Nickl and Benedikt M. Pötscher},
journal= {arXiv preprint arXiv:0908.0433},
year = {2011}
}
Comments
Minor revision, some references and remarks added