I am using Stata 13 to investigate a local programm aimed at reducing obesity amoung teenagers and adolescents in a school. I have a balanced panel over twelve years and a couple of covariates (like age and gender and a few more) over time. Some participants get some treatment/support if their school-id is odd. Indications are however strong that some participants are prefered. Let me illustrate with some fictive generated data:
*Gen sample data set obs 120 egen year = seq(), from(1) to(12) bysort year: egen id = seq(), from(1) to(10) xtset id year *Gen y- and x-list, only odd ids get treatment in odd years if x1>2 gen odd = mod(id, 2) gen x1 = runiform()*10 gen x2 = runiform()*10 gen age = round(runiform()*20) if year==1 replace age = L1.age+1 if year>1 gen gender = round(runiform()) if year ==1 replace gender = L1.gender if year>1 gen treat = 0 replace treat = 1 if odd==1 & x1>=2 drop odd gen y = L1.y - gender + age + x1 + x2 - treat + runiform() *Gen difference gen d_y = y-L1.y
The data provides us with 12 years of data for 10 participants. The participants get the treatment if their id is odd and if x1 is larger or equal to two. Their weight y is path dependend and further defined by their age, their gender, x1, x2, and of course the treatment.
I decided to use propensity score matching. Here is what I did:
*Propensity score matching pscore treat gender age x1 x2, pscore(score) blockid(block) comsup *match *e.g. nearest neigbour attnd d_y treat gender age x1 x2 , pscore(score) boot reps(5) comsup
I first calculated the propensity to get a treatment based ont the participants covariates. I then used the nearest-neighbour approach to pair treatment and controls. However, I used the yearly difference in y (d_y) as a dependent variable to take account of the panel structure.
I have the following questions:
- Is the procedure correct? Particularly, is it enough to take the the yearly difference to make use of the panel structure?
- Given time varying and constant covariates over time, is it possible to combine PSM with random/fixed effect estimators? Such as using
xtprobitto calculate the propensity score for each year?
- Are their nice ways to graph the region of common support?