Model-implied instrumental variable-generalized method of moments (MIIV-GMM) estimators for latent variable models.

Model-implied instrumental variable-generalized method of moments (MIIV-GMM) estimators for latent variable models.
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DOI:
10.1007/s11336-013-9335-3
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发表时间:
2014-01
期刊:
影响因子:
3
通讯作者:
Bauldry S
Bauldry S
中科院分区:
心理学4区
文献类型:
--
作者:
Bollen KA;Kolenikov S;Bauldry S

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结构方程模型的常用极大似然估计在理想条件下(如结构正确、无过多峰度等)具有最优的渐近性质。在实践中很少遇到。本文提出了隐含工具变量-广义矩方法(MIIV-GMM)的隐含工具变量-广义矩估计(MIIV-GMM),该估计对模型结构和分布假设的违反比ML更稳健。在要求较低的假设下,MIIV-GMM估计是一致的,渐近无偏的,渐近正态的,并且有一个渐近协方差矩阵。它们是“无分布的”,对异方差具有健壮性,并且具有对渐近X平方分布的J检验的过度识别优度。此外,MIIV-GMM估计器具有“可伸缩性”,因为它们可以估计和测试整个模型或方程的任何子集,因此可以更好地精确定位模型中符合和不符合数据的部分。一个实证例子说明了MIIV-GMM估计量。一项模拟研究探索了它们的有限样本性质,发现它们在一定的样本大小范围内表现良好。
The common Maximum Likelihood (ML) estimator for structural equation models (SEMs) has optimal asymptotic properties under ideal conditions (e.g., correct structure, no excess kurtosis, etc.) that are rarely met in practice. This paper proposes Model Implied Instrumental Variable - Generalized Method of Moments (MIIV-GMM) estimators for latent variable SEMs that are more robust than ML to violations of both the model structure and distributional assumptions. Under less demanding assumptions the MIIV-GMM estimators are consistent, asymptotically unbiased, asymptotically normal, and have an asymptotic covariance matrix. They are “distribution-free”, robust to heteroscedasticity, and have overidentification goodness of fit J tests with asymptotic chi square distributions. In addition, MIIV-GMM estimators are “scalable” in that they can estimate and test the full model or any subset of equations and hence allow better pinpointing of those parts of the model that fit and do not fit the data. An empirical example illustrates MIIV-GMM estimators. A simulation study explores their finite sample properties and finds that they perform well across a range of sample sizes.
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