Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference
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 Cramér-Rao bound.
|Date of creation:||Mar 2009|
|Date of revision:|
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Web page: https://mpra.ub.uni-muenchen.de
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