a University of North Carolina at Chapel Hill.
Multivariate Behav Res. 2019 Mar-Apr;54(2):246-263. doi: 10.1080/00273171.2018.1519406. Epub 2019 Mar 4.
Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.
结构方程建模（SEM）是一种越来越流行的方法，用于检验多元时间序列数据。与横截面数据分析一样，时间序列模型的结构误设定是不可避免的，而且由于模型的时间序列和测量分量都存在误差，情况更加复杂。在本文中，我们在 SEM 框架内引入了一种新的有限信息估计量和局部拟合诊断方法，用于动态因子模型。我们通过一个小型模拟研究展示了该估计量的实现，并在正确和不正确的模型规范下检查了其性能。该估计量的估计值与最常见的系统范围估计值进行了比较，结果发现它对所考虑的结构误设定更具鲁棒性。
