Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure
复制标题
协方差结构非线性模型估计量的小样本性质
DOI：
10.1080/07350015.1996.10524662
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发表时间：
1996
影响因子：
3
通讯作者：
Todd E. Clark
中科院分区：
文献类型：
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作者：
Todd E. Clark
本文研究了协方差结构非线性模型的GMM和ML估计的小样本性质。研究的重点是参数估计的性质和汉森（1982）和纽维（1985）模型规格检验。它使用Monte Carlo模拟来考虑一些简单因子模型的估计性质，Hall和Mishkin（1982）的消费和收入变化模型，以及一个简单的Bernanke（1986）分解模型。这一分析确立并试图解释一些结果。最重要的是，最优加权GMM估计产生一些有偏的参数估计，和GMM估计产生的模型规格测试的大小大大大于渐近大小。
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.