The autoregressive model is a useful tool to analyze longitudinal data. It is particularly suitable for gerontological research as autoregressive models can be used to establish the causal relationship within a single variable over time as well as the causal ordering between two or more variables (e.g., physical health and psychological well-being) over time through bivariate autoregressive cross-lagged or contemporaneous models. Specifically, bivariate autoregressive models can explore the cross-lagged effects between two variables over time to determine the proper causal ordering between these variables. The advantage of analyzing cross-lagged effects is to test for the strength of prediction between two variables controlling for each variable's previous time score as well as the autoregressive component of the model. Bivariate autoregressive contemporaneous models can also be used to determine causal ordering within the same time point when compared to cross-lagged effects. Since the technique uses structural equation modeling, models are also adjusted for measurement error. This paper will present an introduction to setting up models and a step-by-step approach to analyzing univariate simplex autoregressive models, bivariate autoregressive cross-lagged models, and bivariate autoregressive contemporaneous models.

自回归模型是分析纵向数据的有用工具。它特别适用于老年学研究，因为自回归模型可用于建立单个变量内随时间推移的因果关系，以及通过双变量随时间推移建立两个或多个变量（例如，身体健康和心理健康）之间的因果顺序自回归交叉滞后或同期模型。具体来说，双变量自回归模型可以探索两个变量之间随时间推移的交叉滞后效应，以确定这些变量之间的正确因果顺序。分析交叉滞后效应的优点是测试两个变量之间的预测强度，控制每个变量的先前时间得分以及模型的自回归分量。与交叉滞后效应相比，双变量自回归同期模型也可用于确定同一时间点内的因果排序。由于该技术使用结构方程建模，因此模型也会针对测量误差进行调整。本文将介绍建立模型和逐步分析单变量单纯形自回归模型、双变量自回归交叉滞后模型和双变量自回归同期模型的方法。