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On Bahadur efficiency of empirical likelihood

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  • Otsu, Taisuke

Abstract

This paper studies the Bahadur efficiency of empirical likelihood for testing moment condition models. It is shown that under mild regularity conditions, the empirical likelihood overidentifying restriction test is Bahadur efficient, i.e., its p-value attains the fastest convergence rate under each fixed alternative hypothesis. Analogous results are derived for parameter hypothesis testing and set inference problems.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 157 (2010)
Issue (Month): 2 (August)
Pages: 248-256

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Handle: RePEc:eee:econom:v:157:y:2010:i:2:p:248-256

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Empirical likelihood Bahadur efficiency Large deviation GMM;

References

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  1. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol. 75(5), pages 1243-1284, 09.
  2. Yuichi Kitamura & Andres Santos & Azeem M. Shaikh, 2012. "On the Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions," Econometrica, Econometric Society, vol. 80(1), pages 413-423, 01.
  3. Hong, Han & Preston, Bruce & Shum, Matthew, 2003. "Generalized Empirical Likelihood Based Model Selection Criteria For Moment Condition Models," Econometric Theory, Cambridge University Press, vol. 19(06), pages 923-943, December.
  4. Yuichi Kitamura, 2001. "Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions," Econometrica, Econometric Society, vol. 69(6), pages 1661-1672, November.
  5. Taisuke Otsu, 2009. "Generalized Neyman–Pearson optimality of empirical likelihood for testing parameter hypotheses," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(4), pages 773-787, December.
  6. Guido W. Imbens & Phillip Johnson & Richard H. Spady, 1995. "Information Theoretic Approaches to Inference in Moment Condition Models," NBER Technical Working Papers 0186, National Bureau of Economic Research, Inc.
  7. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
  8. Han Hong & Bruce Preston & Matthew Shum, 2001. "Empirical Likelihood-Based Selection Criteria for Moment Condition Models," Economics Working Paper Archive 459, The Johns Hopkins University,Department of Economics.
  9. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
  10. Donald W.K. Andrews & Patrik Guggenberger, 2007. "The Limit of Finite-Sample Size and a Problem with Subsampling," Cowles Foundation Discussion Papers 1605, Cowles Foundation for Research in Economics, Yale University.
  11. Canay, Ivan A., 2010. "EL inference for partially identified models: Large deviations optimality and bootstrap validity," Journal of Econometrics, Elsevier, vol. 156(2), pages 408-425, June.
  12. 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.
  13. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-80, July.
  14. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
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Cited by:
  1. Canay, Ivan A. & Otsu, Taisuke, 2012. "Hodges–Lehmann optimality for testing moment conditions," Journal of Econometrics, Elsevier, vol. 171(1), pages 45-53.

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