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st: RE: sample size: linear probability vs probit


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: sample size: linear probability vs probit
Date   Mon, 7 Feb 2005 21:59:38 -0000

I think that -probit- and -logit- have
special code to trap those situations. 

If you are fitting a linear probability 
model by -regress-, that has no sense 
of such special problems with binary outcomes. 

(a guess)

Nick 
n.j.cox@durham.ac.uk 

David K Evans

> I understand why the probit model drops variables which 
> predict an outcome
> perfectly even if they aren't perfectly correlated with the 
> outcome (e.g.
> if X=1 always implies Y=1, even if X=0 may not imply that Y=1).
> 
> However, the linear probability model does not drop those variables.

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