Lehrstuhl für Psychologische Methodenlehre & Diagnostik, Department Psychologie, Ludwig-Maximilians-Universität München, Munich, Germany.
Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Multivariate Behav Res. 2024 Sep-Oct;59(5):1019-1042. doi: 10.1080/00273171.2024.2354232. Epub 2024 Jul 26.
There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.
人们越来越呼吁使用具有测量误差的多元时间序列数据进行建模。潜在因素与向量自回归（VAR）模型的结合产生了动态因子模型（DFM），其中动态关系在因子序列内、因子之间和观测时间序列之间或两者之间得出。然而，当前的 DFM 代表和估计存在一些局限性：（1）动态分量包含有向或无向同期关系，但不是两者都有；（2）在探索性 DFM 中选择最佳模型是一个挑战；（3）模型选择的结构误定后果几乎没有研究。我们的论文旨在通过混合 VAR 表示和利用 LASSO 正则化来选择动态隐含工具变量、两阶段最小二乘法（MIIV-2SLS）估计来推进 DFM。我们提出的方法强调了使用稳健估计灵活地对动态关系的方向进行建模。我们旨在为以人为中心的动态评估中的模型选择和估计提供研究人员的指导。
