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Further Results on the Limiting Distribution of GMM Sample Moment Conditions
Authors:Nikolay Gospodinov  Raymond Kan  Cesare Robotti
Institution:1. Department of Economics , Concordia University , Montreal , Quebec , Canada , H3G 1M8;2. Joseph L. Rotman School of Management , University of Toronto , Toronto , Ontario , Canada , M5S 3E6;3. Research Department , Federal Reserve Bank of Atlanta , Atlanta , GA , 30309
Abstract:In this article, we examine the limiting behavior of generalized method of moments (GMM) sample moment conditions and point out an important discontinuity that arises in their asymptotic distribution. We show that the part of the scaled sample moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a nonstandard limiting distribution. We derive the appropriate asymptotic (weighted chi-squared) distribution when this degeneracy occurs and show how to conduct asymptotically valid statistical inference. We also propose a new rank test that provides guidance on which (standard or nonstandard) asymptotic framework should be used for inference. The finite-sample properties of the proposed asymptotic approximation are demonstrated using simulated data from some popular asset pricing models.
Keywords:Asymptotic approximation  Generalized method of moments  Rank test  T-consistent estimator  Weighted chi-square distribution
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