Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
研究广义矩方法和最大似然估计在协方差结构非线性模型中的小样本性质，发现最优加权GMM会产生有偏参数估计和过大的模型检验规模，减少过度识别约束可缓解这些问题。
This study examines the small-sample properties of generalized method of moments (GMM) and maximum likelihood estimators of nonlinear models of covariance structure. It considers the properties of estimates for a simple factor model, the Hall and Mishkin 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.