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AVERAGING OF AN INCREASING NUMBER OF MOMENT CONDITION ESTIMATORS

Published online by Cambridge University Press:  29 December 2014

Xiaohong Chen ,
David T. Jacho-Chávez  and
Oliver Linton
Xiaohong Chen
Affiliation:
Yale University
David T. Jacho-Chávez
Affiliation:
Emory University
Oliver Linton*
Affiliation:
University of Cambridge
*
*Address correspondence to Oliver Linton, Department of Economics, University of Cambridge, Austin Robinson Building, Sidgwick Avenue, Cambridge CB3 9DD, United Kingdom; e-mail: obl20@cam.ac.uk.
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Abstract

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We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of $\sqrt n$ -consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achieves similar objectives of aggregating the available information. One advantage of aggregating the estimators rather than the moment conditions is that it yields robustness to certain types of parameter heterogeneity in the sense that it delivers consistent estimates of the mean effect in that case. We discuss the question of optimal weighting of the estimators.

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ARTICLES
Information
Econometric Theory , Volume 32 , Issue 1 , February 2016 , pp. 30 - 70
Copyright
Copyright © Cambridge University Press 2014 

References

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