Hi all,

I am running a generalized DD to estimate the effect on infant mortality rate (imr) of a health policy implemented at the municipal-level with staggered implementation 2010 - 2017.

Following advice on a previous thread, I have run the regression as follows:
Code:
Code:
xtreg imr i.active_treat i.year Z, fe vce(cluster mun)
where treat =1 if the municipality ever implemented the policy and =0 otherwise, active_treat =1 in all periods when the policy is active and =0 for all periods before policy adoption, as well as =0 in control municipalities for all periods. Z is a vector of municipal time-variant characteristics, such as population, GDP, literacy rate, etc.

My first question is how to allow for time-varying treatment effects? Can I simply create an index variable, s, indexing lead periods before treatment and a variable, m, indexing lag periods? For example:
Code:
xtreg imr i.active_treat i.year i.s i.m Z, fe vce(cluster mun)
Secondly, the policy differed across treated municipalities. Treated municipalities received treatment type1, type2, or both type1 and type2. Is there a way to incorporate this into the model to evaluate the heterogeneous treatment effects across treatment type? A DDD wouldn't work considering only treatment groups receive type1 or type2. Is the only way to estimate the regression on a subsample by treatment type?

Many thanks for the help!