CIRJE-F-430 | "Empirical Likelihood Methods in Econometrics: Theory and Practice" |
Author Name | Kitamura, Yuichi |
Date | June 2006 |
Full Paper | PDF file@ |
Remarks | @ |
Abstract |
Recent developments in empirical likelihood (EL) methods are reviewed. First, to put the
method in perspective, two interpretations of empirical likelihood are presented, one as a nonparametric
maximum likelihood estimation method (NPMLE) and the other as a generalized minimum contrast
estimator (GMC). The latter interpretation provides a clear connection between EL, GMM, GEL and
other related estimators. Second, EL is shown to have various advantages over other methods. The
theory of large deviations demonstrates that EL emerges naturally in achieving asymptotic optimality
both for estimation and testing. Interestingly, higher order asymptotic analysis also suggests that EL
is generally a preferred method. Third, extensions of EL are discussed in various settings, including
estimation of conditional moment restriction models, nonparametric specification testing and time
series models. Finally, practical issues in applying EL to real data, such as computational algorithms
for EL, are discussed. Numerical examples to illustrate the efficacy of the method are presented.
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