Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person.

中文翻译：

---

使用混合组迭代多模型估计估计时间序列数据中的有向和无向同期关系。

不同领域的研究人员越来越多地收集和分析密集的纵向数据 (ILD)，以在个体层面检查跨时间的过程。通常检查两种类型的关系：滞后的和同期的。滞后关系捕获先前时间点的变量如何用于解释稍后时间点变量的方差。这些总是通过向量自回归 (VAR) 使用自回归和交叉回归建模。相比之下，有两种类型的关系通常用于模拟同期关系，它们模拟变量如何即时相关。到目前为止，研究人员必须选择将同期关系建模为残差之间的无向关系（例如，部分或完全相关）或变量之间的有向关系（例如，路径或回归）。选择如何对同期关系建模对推论有影响，如果使用错误的关系类型，也有可能在 VAR 滞后关系中引入偏差。本文介绍了一种新的数据驱动方法，即混合组迭代多模型估计 (GIMME)，该方法解决了必须选择一种或另一种类型的同期关系与模型的问题。除了标准 VAR 关系之外，混合 GIMME 中使用的建模框架还允许两种类型的同期关系。模拟和经验数据都用于测试 hybrid-GIMME 的性能。结果表明，这是一种以探索性方式恢复同期关系的可靠方法，尤其是在每个人有大量时间点的情况下。这为必须选择一种或另一种类型的同期关系模型的问题提供了解决方案。除了标准 VAR 关系之外，混合 GIMME 中使用的建模框架还允许两种类型的同期关系。模拟和经验数据都用于测试 hybrid-GIMME 的性能。结果表明，这是一种以探索性方式恢复同期关系的可靠方法，尤其是在每个人有大量时间点的情况下。这为必须选择一种或另一种类型的同期关系模型的问题提供了解决方案。除了标准 VAR 关系之外，混合 GIMME 中使用的建模框架还允许两种类型的同期关系。模拟和经验数据都用于测试 hybrid-GIMME 的性能。结果表明，这是一种以探索性方式恢复同期关系的可靠方法，尤其是在每个人有大量时间点的情况下。