Econometric Theory



GENERALIZED EMPIRICAL LIKELIHOOD–BASED MODEL SELECTION CRITERIA FOR MOMENT CONDITION MODELS


Han  Hong  a1 c1 , Bruce  Preston  a1 and Matthew  Shum  a2
a1 Princeton University
a2 Johns Hopkins University

Article author query
hong h   [Google Scholar] 
preston b   [Google Scholar] 
shum m   [Google Scholar] 
 

Abstract

This paper proposes model selection criteria (MSC) for unconditional moment models using generalized empirical likelihood (GEL) statistics. The use of GEL-statistics in lieu of J-statistics (in the spirit of Andrews, 1999, Econometrica 67, 543–564; and Andrews and Lu, 2001, Journal of Econometrics 101, 123–164) leads to an alternative interpretation of the MSCs that emphasizes the common information-theoretic rationale underlying model selection procedures for both parametric and semiparametric models. The result of this paper also provides a GEL-based model selection alternative to the information criteria–based nonnested tests for generalized method of moments models considered in Kitamura (2000, University of Wisconsin). The results of a Monte Carlo experiment are reported to illustrate the finite-sample performance of the selection criteria and their impact on parameter estimation. a


Correspondence:
c1 Address correspondence to: Han Hong, Department of Economics, Fisher Hall, Princeton University, Princeton, NJ 08544, USA; e-mail: doubleh@phoenix.Princeton.edu.


Footnotes

a The authors gratefully acknowledge support from the NSF (Hong: SES-0079495, Shum: SES-0003352) and the Fellowship of Woodrow Wilson Scholars (Preston). We thank the co-editor Don Andrews, Xiaohong Chen, John Geweke, Bo Honore, Yuichi Kitamura, Serena Ng, Harry Paarsch, Gautam Tripathi, and two anonymous referees for insightful suggestions and helpful comments.



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