Hi there,

I have two-period balanced panel data (200 individuals in both periods), with which I have to estimate the effect of binary treatment "treated" on outcome variable y (I also have two continuous IVs: x1 and x2).


I need to implement PSM 3 nearest neighbor matching (I do this with -psmatch2-), and thereafter perform a DID regression with the conditioning variables used to estimate the propensity score included as control variables in this regression.


I see many people just using the weights constructed by -psmatch2- in the regression. However, this does not take into account which treatment id is matched with which 3 control id's.


My questions are:

1) Am I correct in thinking that just using the weights in the DID regression is not enough?

2) How do I solve this problem? As stated before, x1 and x2 need to be added as controls in the DID regression as well.

3) Does the method used to solve this change when kernel matching is used?



Example Dataset
Code:
/* install -xfill- by typing net from https://www.sealedenvelope.com/ and clicking on the name */
use https://www.stata-press.com/data/r17/parallelt, clear
keep if inlist(t1,5,6)
rename id1 id 
rename t1 t 
rename y1 y 
replace t = t-5
generate treated = treated1 if t==1
xfill treated, i(id)
order id treated t y x1 x2
keep id-x2


Example of what I mean with just using weights in the regression
Code:
psmatch2 treated x1 x2 if t==0, n(3) caliper(0.1) common 
xfill _weight, i(id)
regress y i.treated##i.t x1 x2 [aw=_weight], robust cluster(id)