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GEL methods for non-smooth moment indicators

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  • Paulo Parente
  • Richard Smith

    ()
    (Institute for Fiscal Studies and University of Cambridge)

Abstract

This paper considers the first order large sample properties of the GEL class of estimators for models specified by non-smooth indicators. The GEL class includes a number of estimators recently introduced as alternatives to the efficient GMM estimator which may suffer from substantial biases in finite samples. These include EL, ET and the CUE. This paper also establishes the validity of tests suggested in the smooth moment indicators case for over-dentifying restrictions and specification. In particular, a number of these tests avoid the necessity of providing an estimator for the Jacobian matrix which may be problematic for the sample sizes typically encountered in practice.

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File URL: http://cemmap.ifs.org.uk/wps/cwp1908.pdf
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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP19/08.

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Date of creation: Jul 2008
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Handle: RePEc:ifs:cemmap:19/08

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References

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  1. Gordon Kemp, 2007. "Gel Estimation and Inference with Non-Smooth Moment Indicators and Dynamic Data," Economics Discussion Papers 640, University of Essex, Department of Economics.
  2. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
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  6. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
  7. 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.
  8. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-47, July.
  9. Donald W.K. Andrews, 1996. "A Stopping Rule for the Computation of Generalized Method of Moments Estimators," Cowles Foundation Discussion Papers 1120, Cowles Foundation for Research in Economics, Yale University.
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  17. 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.
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  19. Whitney K. Newey & Joaquim J. S. Ramalho & Richard Smith, 2003. "Asymptotic bias for GMM and GEL estimators with estimated nuisance parameters," CeMMAP working papers CWP05/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  27. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
  28. Patrik Buggenberger & Richard Smith, 2003. "Generalized empirical likelihood estimators and tests under partial, weak and strong identification," CeMMAP working papers CWP08/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  33. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
  34. Arulampalam, W. & Robin A. Naylor & Jeremy P. Smith, 2002. "University of Warwick," Royal Economic Society Annual Conference 2002 9, Royal Economic Society.
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Citations

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Cited by:
  1. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
  2. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
  3. Feng, Qiang, 2012. "A GEL-based AIC for model selection," Economics Letters, Elsevier, vol. 116(3), pages 637-639.

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