Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
通过蒙特卡洛模拟，研究非线性协方差结构模型中GMM和ML估计量的小样本性质，发现最优加权GMM估计存在参数偏误，且其模型设定检验的实际规模远大于渐近规模。
This study examines the small sample properties of GMM and ML estimators of non-linear models of covariance structure. The study focuses on the properties of parameter estimates and the Hansen (1982) and Newey (1985) model specification test. It use Monte Carlo simulations to consider the properties of estimates for some simple factor models, the Hall and Mishkin (1982) model of consumption and income changes, and a simple Bernanke (1986) decomposition model. This analysis establishes and seeks to explain a number of results. Most importantly, optimally weighted GMM estimation yields some biased parameter estimates, and GMM estimation yields a model specification test with size substantially greater than the asymptotic size.