On Cross-Lagged Panel Models With Serially Correlated Errors
针对交叉滞后面板研究，将回归模型扩展为多元模型，以捕捉变量间的相关性并允许误差随时间相关，帮助研究者更准确估计和检验两组变量间的交叉效应。
Cross-lagged panel studies are studies in which two or more variables are measured for a large number of subjects at each of several points in time. The variables divide naturally into two sets, and the purpose of the analysis is to estimate and test the cross-effects between the two sets. One approach to this analysis is to treat the cross-effects as parameters in regression equations. This study contributes to this approach by extending the regression model to a multivariate model that captures the correlation among the variables and allows the errors in the model to be correlated over time.