Nearly-singular design in GMM and generalized empirical likelihood estimators
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.
Year of publication: |
2008
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Authors: | Caner, Mehmet |
Published in: |
Journal of Econometrics. - Elsevier, ISSN 0304-4076. - Vol. 144.2008, 2, p. 511-523
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Publisher: |
Elsevier |
Saved in:
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