DID models are used to estimate the average treatment effect on the treated (ATET) with repeated- measures data. A treatment effect can be an effect of a drug regimen on blood pressure or an effect of a training program on employment. Unlike with the standard cross-sectional analysis, provided by the existing teffects command, DID analysis controls for group and time effects when estimating the ATET, where groups identify repeated measures.
Heterogeneous DID models additionally account for variation in treatment effects arising from groups being treated at different points in time and effects varying over time within groups.
Suppose that several school districts introduce an exercise and a nutrition program to improve students' health outcomes. Different school districts introduce the program at different points in time. Is it sensible to assume the effect of the program on students’ health outcomes does not change over time and is the same regardless of when the program was adopted? Maybe not. We can use heterogeneous DID models to account for the potential differences in effects.
The new commands hdidregress and xthdidregress fit heterogeneous DID models. hdidregress works with repeated-cross-sectional data, and xthdidregress works with longitudinal/panel data.