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Effcient M-estimators with auxiliary information

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  • F Bravo

Abstract

This paper introduces a new class of M-estimators based on generalised empirical likelihood estimation with some auxiliary information available in the sample. The resulting class of estimators is efficient in the sense that it achieves the same asymptotic lower bound as that of the efficient generalised method of moment-based M-estimator with the same auxiliary information. The results of the paper are quite general and apply to M-estimators defined by both smooth and nonsmooth estimating equations. Simulations show that the proposed estimators perform well in finite samples, and can be less biased and more precise than standard M-estimators within China.

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Bibliographic Info

Paper provided by Department of Economics, University of York in its series Discussion Papers with number 08/26.

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Date of creation: Aug 2008
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Handle: RePEc:yor:yorken:08/26

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Keywords: Asymptotic efficiency. Generalised empirical likelihood. Generalised method of moments. M-estimators. Generalised method of moments; M-estimators.;

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References

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  1. Donald W.K. Andrews, 1993. "Empirical Process Methods in Econometrics," Cowles Foundation Discussion Papers 1059, Cowles Foundation for Research in Economics, Yale University.
  2. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(01), pages 74-113, February.
  3. P. Hall & B. Presnell, 1999. "Intentionally biased bootstrap methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 143-158.
  4. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
  5. Guido W. Imbens & Richard H. Spady & Phillip Johnson, 1998. "Information Theoretic Approaches to Inference in Moment Condition Models," Econometrica, Econometric Society, vol. 66(2), pages 333-358, March.
  6. Guido W. Imbens & Judith K. Hellerstein, 1996. "Imposing Moment Restrictions from Auxiliary Data by Weighting," NBER Technical Working Papers 0202, National Bureau of Economic Research, Inc.
  7. Smith, Richard J, 1997. "Alternative Semi-parametric Likelihood Approaches to Generalised Method of Moments Estimation," Economic Journal, Royal Economic Society, vol. 107(441), pages 503-19, March.
  8. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, 01.
  9. repec:cup:cbooks:9780521496032 is not listed on IDEAS
  10. Bruce Brown & Song Chen, 1998. "Combined and Least Squares Empirical Likelihood," Annals of the Institute of Statistical Mathematics, Springer, vol. 50(4), pages 697-714, December.
  11. Imbens, Guido W & Lancaster, Tony, 1994. "Combining Micro and Macro Data in Microeconometric Models," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 655-80, October.
  12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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Cited by:
  1. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
  2. Seojeong Lee, 2014. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators," Discussion Papers 2014-02, School of Economics, The University of New South Wales.

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