Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix

Wei-Ming Lee and Chung-Ming Kuan ()
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Wei-Ming Lee: Department of Economics National Chung Cheng University

No 06-A009, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan

Abstract: We extend the KVB approach of Kiefer, Vogelsang, and Bunzel (2000, Econometrica) and Kiefer and Vogelsang (2002b, Econometric Theory) to construct a class of robust tests for over-identifying restrictions in the context of GMM. The proposed test does not require consistent estimation of the asymptotic covariance matrix but relies on kernel-based normalizing matrices to eliminate the nuisance parameters in the limit. Moreover, the proposed test is valid for any consistent GMM estimator, in contrast with the conventional test that requires the optimal GMM estimator, and hence is easy to implement. Our simulations show that the proposed test is properly sized and may even be more powerful than the conventional test computed with an inappropriate user-chosen parameter.

Keywords: generalized method of moments; kernel function; KVB approach; overidentifying restrictions; robust test (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2006-09
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Related works:
Journal Article: Testing over-identifying restrictions without consistent estimation of the asymptotic covariance matrix (2014)
Working Paper: Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix (2014)
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