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GENERALIZED EMPIRICAL LIKELIHOOD INFERENCE FOR NONLINEAR AND TIME SERIES MODELS UNDER WEAK IDENTIFICATION

Published online by Cambridge University Press:  15 March 2006

Taisuke Otsu
Taisuke Otsu
Affiliation:
Cowles Foundation, Yale University
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Abstract

This paper studies robust inference methods for nonlinear moment restriction models with weakly identified parameters in time series contexts. Our methods are based on generalized empirical likelihood with kernel smoothing. The proposed test statistics, which follow the standard χ2 limiting distributions, are robust to weak identification and dependent data.The author is deeply grateful to Bruce Hansen, John Kennan, and Gautam Tripathi for their guidance and time. Comments from a coeditor and two anonymous referees substantially helped this revision. The author also thanks Allan Gregory, Patrik Guggenberger, Philip Haile, Hiroyuki Kasahara, Matthew Kim, Yuichi Kitamura, and seminar participants at Queen's University, University of Wisconsin, and the 2003 North America Summer Meeting of the Econometric Society for helpful discussions and suggestions. Financial support from the Alice Gengler Wisconsin Distinguished Graduate Fellowship and Wisconsin Alumni Research Foundation Dissertation Fellowship is gratefully acknowledged.

Type
MISCELLANEA
Information
Econometric Theory , Volume 22 , Issue 3 , June 2006 , pp. 513 - 527
Copyright
© 2006 Cambridge University Press

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