Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6870067 | Computational Statistics & Data Analysis | 2014 | 25 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Rachidi Kotchoni,