Hi,

This might be more of an advanced econometrics question rather than a Stata question. I have 13 dependent variables [wd_1, wd_2... wd_13] each with five levels (0,1,2,3, 99). These levels are ordinal (except missing). I also have one independent variable [to_whom] with seven levels (0,1,2,3,4,5,6). These levels are also ordinal and have no missing values.

Is using a multinomial probit regression with this many dependent variables with this many levels correct to do? Or would a different model make sense? I believe this model to make sense and wanted to know if the correct code to input for all 13 dependent variables would simply just be putting their names in after global ylist.

I would not try to run this with this many regressions as the number of iterations necessary is quite high. But I wanted to post code to see if this is a correct set up with what I am trying to accomplish.

* Dependent variable has 5 categories denoted 0,1,2,3,99
global ylist wd_1 wd_2 wd_3 wd_4 wd_5 wd_6 wd_7 wd_8 wd_9 wd_10 wd_11 wd_12 wd_13
global xlist to_whom
describe $ylist $xlist
summarize $ylist $xlist
tabulate $ylist
* Multinomial probit model with base outcome the most frequent alternative
mprobit $ylist $xlist
* Multinomial probit with base outcome alternative 2
mprobit $ylist $xlist
* Multinomial probit marginal effects
margins, dydx(*) atmeans predict(pr outcome(0))
margins, dydx(*) atmeans predict(pr outcome(1))
margins, dydx(*) atmeans predict(pr outcome(2))
margins, dydx(*) atmeans predict(pr outcome(3))
margins, dydx(*) atmeans predict(pr outcome(99))

predict p1, outcome(0)
predict p2, outcome(1)
predict p3, outcome(2)
predict p4, outcome(3)
predict p5, outcome(99)

Thanks!