Hi Statalist,

In my paper I validate a theoretical proposition through the following FE regression:
Code:
areg y x1##x2 i.round,a(id)
where y,x1,x2 are binary variables.

There is some concern that there might be reverse causality from y to x1.

Based on Leszczensky, L., & Wolbring, T. (2019) (available here) I am trying to implement the ML-SEM method through the -xtdpdml- command, as robustness check. I have gone through Moral-Benito,Allison,Williams,2016 guide on the command.

Based on my understanding, both x1 and x2 would be predetermined variables in my example. However, I'm not sure how to express the interaction
Code:
x1##x2
while using this command.
According to the guide, the syntax should be
Code:
xtdpdml depvar strictly_exogenous_variable, inv(time_invariant_variable) pre(predetermined variable)
Should the
Code:
x1##x2
go inside the predetermined variable?

Moreover, in my original specification, the time invariant variable was incorporated by absorbing individual level fixed effects through
Code:
,a(id)
. I'm not able to understand how I'm going to incorporate the fixed effects in this syntax, and whether I would need separate set of data for time invariant variables to include under
Code:
inv
.

I understand this question might seem confusing. It's partly because I myself am confused about how this command is working. i would appreciate it if you asked for further clarification if you need to help me out here.

Thanks,


Edited to add: I tried by adding interaction term in pre():
Code:
xtdpdml y, pre(L.x1##L.x2) tfix
and got the following error
Code:
factor variables not allowed
(error in option predetermined())
When I changed i into
Code:
xtdpdml y, pre(L.x1) tfix
, I got
Code:
no observations
r(2000);