Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
This study examines the small sample properties of generalized method of moments (GMM) and maximood likelihood estimators of nonlinear models of covariance structure. It considers the properties of estimates for a simple factor model, the Hall and Mishkin (1982) model of consumption and income, and a simple structural vector-autoregression-type error model. This analysis establishes three basic results. First, optimally weighted GMM estimation yields some biased parameter estimates. Second, GMM estimation yields a model specification test with size substantially greater than the asymptotic size. Third, these problems are mitigated when the number of overidentifying restrictions in a model is reduced.
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Volume (Year): 14 (1996)
Issue (Month): 3 (July)
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