I have a multinomial model with binary variables that includes an interaction term. When I run my regression as:
mlogit x y x#y, I get sensible output with an estimate for the interaction term at values (0 1) and with two ommissions at (1 0) and (1 1), as I'd expect. However, when I then try to run the command mfx, an error is returned: x#0b: operator invalid r(198)
When I pre-generate the interaction term, such that z = x * y and run mlogit x y z, I can get marginal effects out of the model. However, the parameter estimates for y and z (but not x) differ considerably from the former specification and y becomes significantly different from zero (which is not expected).
As best I can tell, this seems to be an issue with how Stata 11 handles interaction terms. If I run version 10.1: mlogit x y x#y, I get an error that interactions not allowed r(101).
Is there a way I can get mfx to work with the model generated by version 11, or could I use something other than marginal effects to get around that?
You report three problems which I cover with examples. Comments within the code explain.
clear all
set more off
webuse sysdsn1
*----- problem 1 -----
// error: -mfx- can't handle factor variable notation (use -margins- instead)
mlogit insure age male nonwhite male#nonwhite i.site
mfx
*----- problem 2 -----
// error: factor variable notation is available only with Stata >= 11
version 10: mlogit insure age male nonwhite male#nonwhite i.site
*----- problem 3 -----
// results are the same
mlogit insure age male nonwhite c.male#c.nonwhite i.site
gen mnw = male * nonwhite
mlogit insure age male nonwhite mnw i.site
Bottomline:
If you have Stata >= 11, factor variable notation (#) is available
and the recommended course of action is to use margins, not mfx.
If you have Stata < 11 you must create your own interactions and you can use mfx.
Finally, a statement of inconsistent results can't be assessed properly if you don't provide actual code reproducing the problem (problem 3).