
From  Kenneth Flamm <kflamm@mail.utexas.edu> 
To  statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu 
Subject  Re: st: linear probability model vs. probit/logit 
Date  Thu, 05 Oct 2006 11:23:05 0500 
Hi,
The previous discussion seemed to assume that the linear probability model is
a simple regression model.
However, the linear probability model should be a twostep weighted regression, that is,
first estimate the regression, then save the predicted value (hat), calculate hat*(1hat),
then reestimate the model with analytic weight N/hat*(1hat).
This procedure is implemented in my old and simple LINPROB (downloadable from my website)
Now, some experiments revealed that linprob and logit use the same observations, because for the
observations that are dropped by logit the correlation between the perfect predictor and hat is one,
and variables causing multicollinearity should be dropped by linear regression as well.
Hope this helps,
Tamas

Tamas Bartus, PhD
Associate Professor, Institute of Sociology and Social Policy
Corvinus University, Budapest
1093 Budapest, Fovam ter 8.
Phone: +3614825290 Fax: +3614825226
Homepage: www.unicorvinus.hu/bartus
 Eredeti üzenet 
Feladó: Joseph Coveney <jcoveney@bigplanet.com>
Dátum: Szerda, Október 4, 2006 7:31 de
Tárgy: Re: st: linear probability model vs. probit/logit
> Ronnie Babigumira wrote (excerpted):
>
> Does it make sense that Stata drops a variable that predicts
> perfectly and
> then goes ahead to drop the observations even when it does not use the
> problem variable in the regression? Any insights into what is going
> on.
> 
> 
>
> Take a look at _Release 9 Reference KQ_ Page 98. This is in the entry
> for logit. At the top of the page, you'll see output of a logistic
> regression using the auto dataset, and with a variable dropped and 10
> observations omitted.
>
> The paragraphs beneath the printout, including the technical note
> at the
> bottom of the page, give the reasoning behind omitting observations
> aftera perfectly predicting variable (but not after a collinear
> predictor) has
> been dropped from the list of candidate predictors.
>
> Joseph Coveney
>
>
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