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A new class of asymptotically efficient estimators for moment condition models

  • Fan, Yanqin
  • Gentry, Matthew
  • Li, Tong
Registered author(s):

    In this paper, we propose a new class of asymptotically efficient estimators for moment condition models. These estimators share the same higher order bias properties as the generalized empirical likelihood estimators and once bias corrected, have the same higher order efficiency properties as the bias corrected generalized empirical likelihood estimators. Unlike the generalized empirical likelihood estimators, our new estimators are much easier to compute. A simulation study finds that our estimators have better finite sample performance than the two-step GMM, and compare well to several potential alternatives in terms of both computational stability and overall performance.

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    File URL: http://www.sciencedirect.com/science/article/B6VC0-524FSJD-1/2/21832d4744a8490ccd0dd7d175f36034
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    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 162 (2011)
    Issue (Month): 2 (June)
    Pages: 268-277

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    Handle: RePEc:eee:econom:v:162:y:2011:i:2:p:268-277
    Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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    1. Patrik Guggenberger, 2008. "Finite Sample Evidence Suggesting a Heavy Tail Problem of the Generalized Empirical Likelihood Estimator," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 526-541.
    2. Yuichi Kitamura & Gautam Tripathi & Hyungtaik Ahn, 2004. "Empirical Likelihood-Based Inference in Conditional Moment Restriction Models," Econometrica, Econometric Society, vol. 72(6), pages 1667-1714, November.
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    8. Pastorello, Sergio & Patilea, Valentin & Renault, Eric, 2003. "Iterative and Recursive Estimation in Structural Nonadaptive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 449-82, October.
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    25. repec:ebl:ecbull:v:3:y:2005:i:13:p:1-6 is not listed on IDEAS
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