Empirical likelihood estimation of conditional moment restriction models with unknown functions

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)8-46
Number of pages39
JournalEconometric Theory
Volume27
Issue number1
DOIs
Publication statusPublished - 2011 Feb 1
Externally publishedYes

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normality
regression
efficiency
Conditional moment restrictions
Empirical likelihood
Likelihood estimation
Estimator
Asymptotic efficiency
Endogeneity
Linear regression model
Rate of convergence
Semiparametric model
Asymptotic normality
Heteroskedasticity

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

Cite this

Empirical likelihood estimation of conditional moment restriction models with unknown functions. / Otsu, Taisuke.

In: Econometric Theory, Vol. 27, No. 1, 01.02.2011, p. 8-46.

Research output: Contribution to journalArticle

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