# RE: st: dprobit and lincom

 From Maarten buis <[email protected]> To [email protected] Subject RE: st: dprobit and lincom Date Fri, 22 Jan 2010 02:53:39 -0800 (PST)

```--- On Thu, 21/1/10, Melanee Thomas wrote:
> what's the appropriate syntax to use to get an
> interpretable value from lincom after using dprobit using
> mfx? I'm assuming such syntax exists and that others have
> used it successfully.....

It sounds like you want to interpret an interaction effect in a probit
model, and you want to interpret the effects as marginal effects. That
is actually a surprisingly complicated problem, see for example the
article by Edward Norton and collegues (2004). A simple alternative is
to use -logit- rather than -probit- and interpret the odds ratios, as
is discussed here: <http://www.maartenbuis.nl/wp/interactions.html>.
If you want to stick to marginal effects and -probit- you can compute
those as in the example below, or you can take a look the -inteff-
command by Edward Norton and collegues (see the reference at the
bottom of this post, and -findit inteff-).

*----------- begin example -----------------------
sysuse auto, clear
gen bad = rep78 <= 3 if rep78 < .

sum mpg if e(sample), meanonly
local xb = _b[_cons] + _b[mpg]*r(mean)
sum weight if e(sample), meanonly
local xb = `xb'+_b[weight]*r(mean)
nlcom (good: normalden(`xb')*_b[mpg]) ///
*-------------- end example --------------------

--- Austin Nichols wrote:
> You should really not use -mfx- or -dprobit- at all, as the marginal
> effect at the mean is not informative for most purposes

I think that is a bit strong. When it comes to interpreting these
interaction effects with marginal effects I would probably use both,
as the average marginal effects not only include differences in
effect but also differences in the distribution of the controll
variables. The marginal effects at the means, by necesity control
for this. So by looking at both I get an idea of how much is due
differences in effects and how much is due to differences in
distributions of the controll variables.

Hope this helps,
Maarten

Edward C. Norton, Hua Wang, and Chunrong Ai (2004) Computing interaction effects and standard errors in logit and probit models. The Stata Journal, 4(2): 154--167.
http://www.stata-journal.com/article.html?article=st0063

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------

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