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From |
Nick Cox <n.j.cox@durham.ac.uk> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Heckman with variables that perfectly predict selection |

Date |
Thu, 26 Aug 2010 15:20:37 +0100 |

If you look at the top of the webpage referred to you will see that it is explained and explicitly dated: "ADO-files What's New Stata Ado-files - November 1998" Unless your version of Stata is really, really old, it does not refer to an innovation as far as you are concerned. Nick n.j.cox@durham.ac.uk Maria Alva I recently came across the following statement in http://www.stata.com/support/updates/ado/whatsnew.html "(STB-43) heckman heckman now has the capability to estimate models with variables that perfectly predict selection. Previously heckman would simply drop such variables from the selection equation, which is inappropriate in most cases." Puzzled, I tried estimating a heckman selection correction model in a data set where death is observed, and where deaths perfectly predict non-responses to questionnaires. heckman y $x, select(selected= death $x) // selected=1 if a person completes a questionnaire this gives me a negative single digit and statistically significant coefficient. If instead I use probit selected death $x as expected, death drops out My question is twofold: what is the innovation in the Stata command that causes death not to be dropped out of the selection equation as it perfectly predicts the selection indicator? and most importantly, what would be a valid instance for which it would be appropriate to include a variable in the selection equation when this variable perfectly predicts selection? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Heckman with variables that perfectly predict selection***From:*Maria Alva <marialiliana.alva@gmail.com>

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