Abstract
A curse of dimensionality arises when using the Continuum-GMM procedure to estimate large dimensional models. Two solutions are proposed, both of which convert the high dimensional model into a continuum of reduced information sets. Under certain regularity conditions, each reduced information set can be used to produce a consistent estimator of the parameter of interest. An indirect CGMM estimator is obtained by optimally aggregating all such consistent estimators. The simulation results suggest that the indirect CGMM procedure makes an efficient use of the information content of moment restrictions.
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