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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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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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  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.
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  8. 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.
  9. Joaquim J.S. Ramalho & Richard J. Smith, 2005. "Goodness of Fit Tests for Moment Condition Models," Economics Working Papers 5_2005, University of √Čvora, Department of Economics (Portugal).
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Cited by:
  1. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
  2. Feng, Qiang, 2012. "A GEL-based AIC for model selection," Economics Letters, Elsevier, vol. 116(3), pages 637-639.
  3. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.

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