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

From   Jn <>
Subject   Re: st: computing average partial effect in nonlinear models using forecasted distribution of x-variables
Date   Tue, 25 Mar 2008 08:22:59 -0400

Thanks for that helpful quote. I did notice if I were to run a
standard linear regression before working with -adjust- command, my
new predicted values were in the right range (around 0.10). But if I
use -logit-, I get values around (0.05) which is way off and makes no
sense. Given that I am trying to use the future projected mean for
some of my x-variables (which is why I am doing running these
postestimation commands in the first place), I don't think there is a
way around fixing my problem if I were using a logit regression. Do
you think it would be far too incorrect for me to run a standard
linear regression just for this purpose only (forecasting future
probability of a positive outcome)? At least I get reasonable
predicted values that way..

- student

On Tue, Mar 25, 2008 at 7:09 AM, Maarten buis <> wrote:
> This is discussed in Buis (2007) "predict and adjust with logistic
>  regression", The Stata Journal, 7(2), pp. 221-226.
>  The reason for the difference is that -logit- implies a non-linear
>  transformation, so it makes a difference whether you first create
>  predicted values and than compute the mean, or when you first compute
>  the means of explanatory variables and than compute a predicted value.
>  To quote from the article: "It is the difference between a typical
>  predicted probability for someone within a group and the predicted
>  probability for someone with typical values on the explanatory
>  variables for someone within that group."
>  Hope this helps,
>  Maarten
>  --- Jn <> wrote:
>  > 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..
>  -----------------------------------------
>  Maarten L. Buis
>  Department of Social Research Methodology
>  Vrije Universiteit Amsterdam
>  Boelelaan 1081
>  1081 HV Amsterdam
>  The Netherlands
>  visiting address:
>  Buitenveldertselaan 3 (Metropolitan), room Z434
>  +31 20 5986715
>  -----------------------------------------
>       ___________________________________________________________
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