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st: computing average partial effect in nonlinear models using forecasted distribution of x-variables


From   Jn <ensam21@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: computing average partial effect in nonlinear models using forecasted distribution of x-variables
Date   Tue, 25 Mar 2008 06:47:53 -0400

Dear Statalisters,

I am trying to get at the magnitude of a change in Pr(y=1|x) by
replacing each explanatory variable with its sample average, save for
my variable of interest, which I was hoping to use a future projected
distribution (I'm trying to see how this change in distribution of
this certain binary independent variable changes the probability of
y=1). I had no problem doing this with linear regressions (replace all
variables with its sample mean, except use projected distribution for
my variable of interest, do a linear prediction, note the difference).
However, when I try to carry out the same procedure in a logit
regression, I am running into problems. I was under the impression
that, if I were to replace ALL of my independent variables with its
sample mean and then run -predict-, I should get the same predicted y
value as if I were to just run a normal regression without replacing
my x-var with its sample mean. Am I wrong? I hope I am making myself
clear..

Any help would be greatly appreciated.

Thanks,
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