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Nearly Singular design in gmm and generalized empirical likelihood estimators

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  • Mehmet Caner

Abstract

Nearly-Singular design relaxes the nonsingularity assumption of the limit weight matrix in GMM, and the nonsingularity of the limit variance matrix for the first order conditions in GEL. The sample versions of these matrices are nonsingular, but in large samples we assume these sample matrices converge to a singular matrix. This can result in size distortions for the overidentifying restrictions test and large bias for the estimators. This nearly-singular design may occur because of the similar instruments in these matrices. We derive the large sample theory for GMM and GEL estimators under nearly-singular design. The rate of convergence of the estimators is slower than root n.

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Bibliographic Info

Paper provided by University of Pittsburgh, Department of Economics in its series Working Papers with number 211.

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Date of creation: Jan 2005
Date of revision: Jan 2005
Handle: RePEc:pit:wpaper:211

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Cited by:
  1. Bertille Antoine & Eric Renault, 2012. "Efficient Minimum Distance Estimation with Multiple Rates of Convergence," Discussion Papers dp12-03, Department of Economics, Simon Fraser University.
  2. Tais Carestiato Da Silva & Helder Ferreira De Mendonça, 2011. "Setting The Interest Rate For Twooutlier Countries," Anais do XXXVIII Encontro Nacional de Economia [Proceedings of the 38th Brazilian Economics Meeting] 207, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
  3. Caner, Mehmet & Yıldız, Neşe, 2012. "CUE with many weak instruments and nearly singular design," Journal of Econometrics, Elsevier, vol. 170(2), pages 422-441.

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