Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference
arXiv:0908.0433
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.
Minor revision, some references and remarks added