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ASYMPTOTIC SIZE OF KLEIBERGEN’S LM AND CONDITIONAL LR TESTS FOR MOMENT CONDITION MODELS

Published online by Cambridge University Press:  03 October 2016

Donald W.K. Andrews  and
Patrik Guggenberger
Donald W.K. Andrews*
Affiliation:
Yale University
Patrik Guggenberger
Affiliation:
Pennsylvania State University
*
*Address correspondence to Donald W.K. Andrews, Cowles Foundation, P.O. Box 208281, New Haven, CT 06520-8281; e-mail: donald.andrews@yale.edu.
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Abstract

An influential paper by Kleibergen (2005, Econometrica 73, 1103–1123) introduces Lagrange multiplier (LM) and conditional likelihood ratio-like (CLR) tests for nonlinear moment condition models. These procedures aim to have good size performance even when the parameters are unidentified or poorly identified. However, the asymptotic size and similarity (in a uniform sense) of these procedures have not been determined in the literature. This paper does so.

This paper shows that the LM test has correct asymptotic size and is asymptotically similar for a suitably chosen parameter space of null distributions. It shows that the CLR tests also have these properties when the dimension p of the unknown parameter θ equals 1. When p ≥ 2, however, the asymptotic size properties are found to depend on how the conditioning statistic, upon which the CLR tests depend, is weighted. Two weighting methods have been suggested in the literature. The paper shows that the CLR tests are guaranteed to have correct asymptotic size when p ≥ 2 when the weighting is based on an estimator of the variance of the sample moments, i.e., moment-variance weighting, combined with the Robin and Smith (2000, Econometric Theory 16, 151–175) rank statistic. The paper also determines a formula for the asymptotic size of the CLR test when the weighting is based on an estimator of the variance of the sample Jacobian. However, the results of the paper do not guarantee correct asymptotic size when p ≥ 2 with the Jacobian-variance weighting, combined with the Robin and Smith (2000, Econometric Theory 16, 151–175) rank statistic, because two key sample quantities are not necessarily asymptotically independent under some identification scenarios.

Analogous results for confidence sets are provided. Even for the special case of a linear instrumental variable regression model with two or more right-hand side endogenous variables, the results of the paper are new to the literature.

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 5 , October 2017 , pp. 1046 - 1080
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

Andrews and Guggenberger gratefully acknowledge the research support of the National Science Foundation via grant numbers SES-1058376 and SES-1355504, and SES-1021101, SES-1326827, and SES-1462707, respectively. The authors thank four referees, the co-editor, the editor Peter Phillips, Isaiah Andrews, Xu Cheng, Anna Mikusheva, and Jim Stock and the participants of seminars at the following universities for helpful comments: Boston, Boston College, Brown, Chicago, Cologne, Columbia, Freiburg, Hanover, Harvard/MIT, Hebrew Jerusalem, Konstanz, Manchester, Maryland, Michigan, Montreal, New York, Northwestern, Ohio State, Pompeu Fabra, Princeton, Queen’s, Strasbourg, University College London, Washington, and Wisconsin.

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