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A new class of asymptotically efficient estimators for moment condition models

Author

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  • Fan, Yanqin
  • Gentry, Matthew
  • Li, Tong

Abstract

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.

Suggested Citation

  • Fan, Yanqin & Gentry, Matthew & Li, Tong, 2011. "A new class of asymptotically efficient estimators for moment condition models," Journal of Econometrics, Elsevier, vol. 162(2), pages 268-277, June.
  • Handle: RePEc:eee:econom:v:162:y:2011:i:2:p:268-277
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    References listed on IDEAS

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    Cited by:

    1. Wang, Xuexin, 2016. "A New Class of Tests for Overidentifying Restrictions in Moment Condition Models," MPRA Paper 69004, University Library of Munich, Germany.
    2. Prosper Dovonon, 2016. "Large Sample Properties of the Three-Step Euclidean Likelihood Estimators under Model Misspecification," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 465-514, April.

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