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Empirical Likelihood-Based Inference in Conditional Moment Restriction Models

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  • Yuichi Kitamura

    (Department of Economics, University of Wisconsin)

  • Gautam Tripathi

    (Department of Economics, University of Wisconsin)

  • Hyungtaik Ahn

    (Department of Economics, Dongguk University)

Abstract

This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples.

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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-124.

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Length: 42 pages
Date of creation: Jul 2001
Date of revision:
Handle: RePEc:tky:fseres:2001cf124

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