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Re: st: linear probability model vs. probit/logit

From   "Tamas Bartus (tbartus)" <>
Subject   Re: st: linear probability model vs. probit/logit
Date   Thu, 05 Oct 2006 16:15:41 +0200


The previous discussion seemed to assume that the linear probability model is
a simple regression model.

However, the linear probability model should be a two-step weighted regression, that is,
first estimate the regression, then save the predicted value (hat), calculate hat*(1-hat),
then reestimate the model with analytic weight N/hat*(1-hat).

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 Bartus, PhD
Associate Professor, Institute of Sociology and Social Policy
Corvinus University, Budapest 
1093 Budapest, Fovam ter 8.
Phone: +36-1-482-5290         Fax:   +36-1-482-5226

----- Eredeti üzenet -----
Feladó: Joseph Coveney <>
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 K-Q_ 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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