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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. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: sample size: linear probability vs probit***From:*SamL <saml@demog.berkeley.edu>

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