Time-varying effect modeling (TVEM) allows scientists to understand the way associations between variables change over time. TVEM is an extension of linear regression that allows the association between two variables to be modeled without making assumptions about the nature of the association. For example, TVEM does not force an estimated curve to be linear.
TVEM was developed for use with intensive longitudinal data (ILD), but it has been expanded to be used with a broad variety of data types. The examples on this page use panel data and ILD, but TVEM has also been applied to cross-sectional data and historical data. There are a handful of characteristics that data must have to apply TVEM.