  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.
