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EMPIRICAL LIKELIHOOD ESTIMATION OF CONDITIONAL MOMENT RESTRICTION MODELS WITH UNKNOWN FUNCTIONS

Published online by Cambridge University Press:  30 April 2010

Taisuke Otsu
Taisuke Otsu*
Affiliation:
Yale University
*
*Address correspondence to Taisuke Otsu, Department of Economics, Yale University, Box 208281, New Haven, CT 06520-8281, USA; e-mail: taisuke.otsu@yale.edu.
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Abstract

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This paper proposes an empirical likelihood-based estimation method for conditional moment restriction models with unknown functions, which include several semiparametric models. Our estimator is called the sieve conditional empirical likelihood (SCEL) estimator, which is based on the methods of conditional empirical likelihood and sieves. We derive (i) the consistency and a convergence rate of the SCEL estimator for the whole parameter, and (ii) the asymptotic normality and efficiency of the SCEL estimator for the parametric component. As an illustrating example, we consider a partially linear regression model with nonparametric endogeneity and heteroskedasticity.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 27 , Issue 1: SPECIAL ISSUE ON EMPIRICAL LIKELIHOOD AND RELATED METHODS , February 2011, pp. 8 - 46
Copyright
Copyright © Cambridge University Press 2010

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