Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure

Todd Clark

Journal of Business & Economic Statistics, 1996, vol. 14, issue 3, 367-73

Abstract: 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.

Date: 1996
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Working Paper: Small sample properties of estimators of non-linear models of covariance structure (1995)
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Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:14:y:1996:i:3:p:367-73

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