Y
Yuichi Kitamura
Researcher at Cowles Foundation
Publications - 46
Citations - 2090
Yuichi Kitamura is an academic researcher from Cowles Foundation. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 20, co-authored 46 publications receiving 1950 citations. Previous affiliations of Yuichi Kitamura include University of Pennsylvania & University of Minnesota.
Papers
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An information-theoretic alternative to generalized method of moments estimation
TL;DR: In this article, the Kullback-Leibler Information Criterion is used for weakly dependent data generating mechanisms, and conditions are derived under which the large sample properties of this estimator are similar to GMM, i.e., the estimator will be consistent and asymptotically normal, with the same covariance matrix as GMM.
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Empirical Likelihood Based Inference in Conditional Moment Restriction Models
TL;DR: This paper proposed an asymptotically efficient method for estimating models with conditional moment restrictions, which generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994).
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Empirical Likelihood Methods in Econometrics: Theory and Practice
TL;DR: In this paper, 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).
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Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions
TL;DR: The authors showed that empirical likelihood and other commonly used tests for moment restrictions are unable to control the (exponential) rate at which the probability of a Type I error tends to zero unless the possible distributions for the observed data are restricted appropriately.
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On the Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions
TL;DR: This article showed that empirical likelihood and other commonly used tests for parametric moment restrictions, including the GMM-based J-test of Hansen (1982), are unable to control the rate at which the probability of a Type I error tends to zero.