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From |
Monica Oviedo <monica.oviedo@uab.cat> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: marginal effects in biprobit and average treatment effect in switching probit |

Date |
Thu, 14 Jun 2012 03:45:09 -0700 (PDT) |

Dear Statalist: I'm estimating the effect of an endogenous dichotomous variable y2 on a dichotomous variable y1 using a recursive biprobit model: biprobit (y1=x y2) (y2=x z) Where z is the exclusion restriction. I'm interested in the marginal effect of y2 on y1, which I think is: E[y1/y2=1] - E[y1/y2=0] I did what Austin Nichols suggested in this thread (namely, the conditional prob of Y1=1 given y2=1 less the conditional probability of Y1=1 given y2=0, letting y2=1 and y2=0 in turn for each observation, and then averaging over observations). In addition, I followed the procedures sugested by him in this file: http://www.stata.com/meeting/chicago11/materials/chi11_nichols.pdf This is: margins, dydx(y2) predict(pmarg1) force I think the latter is correct for estimating what I need. However, I get very different results from both procedures (in the first case a marginal effect of 0.08 vs a marginal effect of 0.45 using the second way). What is the difference between both procedures? Is it supposed that they estimate the same effect? A final question is if biprobit is well suited for estimate the following: biprobit (y1=x y2 x*y2) (y2=x z) This is, if there is any problem when an interaction term between the endogenous variable y2 and a continous x is added. Regards, Monica Oviedo -- View this message in context: http://statalist.1588530.n2.nabble.com/st-marginal-effects-in-biprobit-and-average-treatment-effect-in-switching-probit-tp4494808p7580200.html Sent from the Statalist mailing list archive at Nabble.com. * * 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/

**Follow-Ups**:**Re: st: marginal effects in biprobit and average treatment effect in switching probit***From:*Austin Nichols <austinnichols@gmail.com>

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