Dear all,

I need to estimate bivariate probit model but with panel data, therefore I need something like biprobit but with panel data. Additionaly, I need marginal effects, and conditional probabilities. I know that Roodman cmp commmand can flexibly deal with many different models including panel data structure. I have short panel, N several thousands, and T 4 years.

cmp (y1=$xlist1 || idperson (y2=$xlist1 || idperson, ind($cmp_probit $cmp_probit)
margins, dydx(*) predict(pr eq(y1) condition(0 ., eq(y2))) post // this is probability that y1=1 condition on y2=0
estimate store cmp_y1_cond0
margins, dydx(*) predict(pr eq(y1) condition(1 ., eq(y2))) post // this is probability that y1=1 condition on y2=1
estimate store cmp_y1_cond1

My question is if the above command estimates bivariate random effects probit model or not? Does this command takes into account panel structure? In the command I have explicitiely idperson, should I put dummies for time dimension?

Here is the reference for cmp command
Roodman (2011), Fitting fully observed recursive mixed-process models with cmp, Stata Journal, Vol 11, No 2, 159-206.

Thank you
Best regards,
Aleksandra