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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. Imbens, G.W. & Johnson, P. & Spady, R.H., 1995. "Information Theoretic Approaches to Inference in Movement Condition Models," Economics Papers 99, Economics Group, Nuffield College, University of Oxford.
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    8. Whitney Newey & Richard Smith, 2003. "Higher order properties of GMM and generalised empirical likelihood estimators," CeMMAP working papers CWP04/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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