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st: RE: sample size: linear probability vs probit
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.
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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