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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Marginal effects in (bi)probit models |

Date |
Mon, 17 Mar 2008 12:58:03 -0400 |

Martin Watts <Martin.Watts@newcastle.edu.au>: I can think of a few reasons why your question might have generated no replies, but my best guess is that it is too ambiguous to spark a response. There are a number of kinds of marginal effects that can be calculated after a probit-type model and myriad ways of estimating them, but one is to impute two values to the entire sample and calculate the mean difference in predictions, e.g. sysuse nlsw88 g cxh=collgr*hours g cxw=collgr*wage probit nev collgr wage hours cxh cxw, nolog preserve replace collg=1 * Now redefine every variable in which collgr appears: replace cxh=collgr*hours replace cxw=collgr*wage predict p1, p replace collg=0 replace cxh=collgr*hours replace cxw=collgr*wage predict p0, p g margeff=p1-p0 su margeff restore This type of "brute force" approach is always available, and has many desirable properties. Conceptually, it is like an Average Treatment Effect (ATE) estimate, with no matching or reweighting. To get standard errors, you can wrap the whole thing in a -program- and bootstrap: cap prog drop mfcoll prog mfcoll, eclass probit nev collgr wage hours cxh cxw, nolog g samp=e(sample) preserve replace collg=1 replace cxh=collgr*hours replace cxw=collgr*wage predict p1, p replace collg=0 replace cxh=collgr*hours replace cxw=collgr*wage predict p0, p g margeff=p1-p0 su margeff mat b=r(mean) restore ereturn post b, es(samp) end bs: mfcoll See also -help adjust- for a different approach. On Sun, Mar 16, 2008 at 3:54 PM, Martin Watts <Martin.Watts@newcastle.edu.au> wrote: > I sent the following msg to the list, but surprisingly got no response. Can anyone assist? > Thanks. > >>> Martin Watts <Martin.Watts@newcastle.edu.au> 03/14/08 9:43 AM >>> > I am aware of the Stata code inteff3 which enables the computation of MEs when there is a triple dummy variable interaction term ie > b1x1 + b2x2 + b3x3 + b12x1x2+b13x1x3 + b23x2x3 +b123x1x2x3 > > but I am unaware of code which computes MEs when there is a gender slope dummy attached to a number of independent variables say for a labor force participation equation. Clearly both age and the presence of children are likely to differ in their impact on lfp of women and men. > > So MEs would be required say both for the impact of the presence of a young child on female and male lfp, noting that the change in the dummy variable is impacting on the other variables uin the probit eqn too. > > Can anyone cast any light on this problem. > > Many thanls. * * 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: Marginal effects in (bi)probit models***From:*Martin Watts <Martin.Watts@newcastle.edu.au>

**References**:**st: Marginal effects in (bi)probit models***From:*Martin Watts <Martin.Watts@newcastle.edu.au>

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