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Effcient M-estimators with auxiliary information

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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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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Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom

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  1. 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.
  2. 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.
  3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  4. 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.
  5. 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.
  6. Donald W.K. Andrews, 1993. "Empirical Process Methods in Econometrics," Cowles Foundation Discussion Papers 1059, Cowles Foundation for Research in Economics, Yale University.
  7. Bruce Brown & Song Chen, 1998. "Combined and Least Squares Empirical Likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(4), pages 697-714, December.
  8. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
  9. 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.
  10. Imbens, G.W. & Lancaster, T., 1991. "Combining Micro and Macro Data in Microeconometric Models," Harvard Institute of Economic Research Working Papers 1578, Harvard - Institute of Economic Research.
  11. 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.
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