RE: st: Obtaining marginal effects and their standard errors after estimations with interactions

[Ebru Ozturk <ebru_0512@hotmail.com>]

Yes, that's true but I dont think it is wrong to produce a separate marginal effect. Also this 2004 FAQ is for Stata 10. Maybe that's the reason to still have this information on FAQ page.

----------------------------------------
> Date: Fri, 4 Jan 2013 15:15:58 -0500
> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> From: richardwilliams.ndu@gmail.com
> Subject: RE: st: Obtaining marginal effects and their standard errors after estimations with interactions
>
> At 12:24 PM 1/4/2013, Ebru Ozturk wrote:
> >It's not that hard, just you need to be careful. Stata 10 is the
> >only choice for me. I just need an example that inludes a few more
> >independent and control variables.
> >
> >Ebru
>
> I think it is interesting that the -margins- command works somewhat
> differently than the approach presented in the FAQ. In particular,
> margins does not produce a separate marginal effect for the
> interaction term while the FAQ approach does. This makes me wonder if
> (a) the 2004 FAQ is now considered wrong, or (b) both the FAQ and
> margins approaches are considered legitimate but alternative
> approaches. Personally, I think what margins does is very logical,
> but nonetheless people keep on asking for marginal effects of
> interaction terms.
>
> >----------------------------------------
> > > From: richardwilliams.ndu@gmail.com
> > > Date: Fri, 4 Jan 2013 11:56:50 -0500
> > > Subject: Re: st: Obtaining marginal effects and their standard
> > errors after estimations with interactions
> > > To: statalist@hsphsun2.harvard.edu
> > >
> > > I hate trying to do something like this by hand. Too much room for
> > > error. Can't you tell whoever you work for that you can't be expected
> > > to work under such primitive inhumane conditions and you need Stata
> > > 12?
> > >
> > > You might check out the user-written -inteff- command and see if it
> > > helps. -margeff- is another user-written command that has various
> > > advantages over -mfx-.
> > >
> > > Sent from my iPad
> > >
> > > On Jan 4, 2013, at 11:33 AM, Ebru Ozturk <ebru_0512@hotmail.com> wrote:
> > >
> > > > Thank you, I use Stata 10 therefore I asked this question. I
> > just wonder when we have more independent or control variables how
> > do we adjust the given equations on this link:
> > http://www.stata.com/support/faqs/statistics/marginal-effects-after-interactions/
> > > >
> > > > Kind regards
> > > > Ebru
> > > >
> > > > ----------------------------------------
> > > >> Date: Fri, 4 Jan 2013 09:32:25 -0500
> > > >> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> > > >> From: richardwilliams.ndu@gmail.com
> > > >> Subject: Re: st: Obtaining marginal effects and their standard
> > errors after estimations with interactions
> > > >>
> > > >> At 03:17 PM 1/3/2013, Ebru Ozturk wrote:
> > > >>
> > > >>> Dear All,
> > > >>>
> > > >>> On Stata FAQs' page, there are some given examples for Probit
> > > >>> estimation with interaction effects for Stata 10 titled as "I am
> > > >>> using a model with interactions. How can I obtain marginal effects
> > > >>> and their standard errors?" and the link is:
> > > >>>
> > http://www.stata.com/support/faqs/statistics/marginal-effects-after-interactions/
> > > >>>
> > > >>> Do you think this way is still applicable to Probit estimation? and
> > > >>> Is the below command correct when we have other independent or
> > > >>> control variables?
> > > >>
> > > >> I don't know if you did it right or not, but if you have Stata 11 or
> > > >> higher why not use -margins-, e.g.
> > > >>
> > > >> sysuse auto, clear
> > > >> probit foreign weight length c.weight#c.length, nolog
> > > >> margins, dydx(*)
> > > >>
> > > >>> local xb _b[weight]*`meanwei' + _b[len]*`meanlen' +
> > > >>> _b[wl]*`meanwei'*`meanlen' + _b[C1]*C1+_b[C2]*C2 + _b[_cons] // if
> > > >>> more variables //
> > > >>>
> > > >>> /////// example /////////
> > > >>>
> > > >>> sysuse auto, clear
> > > >>> generate wl=weight*length
> > > >>> probit foreign weight length wl, nolog
> > > >>> quietly summarize weight if e(sample)
> > > >>> local meanwei = r(mean)
> > > >>> quietly summarize length if e(sample)
> > > >>> local meanlen = r(mean)
> > > >>>
> > > >>> local xb _b[weight]*`meanwei' + _b[len]*`meanlen' +
> > > >>> _b[wl]*`meanwei'*`meanlen' + _b[_cons]
> > > >>> predictnl dydw = normalden(`xb')*(_b[weight]+
> > _b[wl]*`meanlen') in 1, se(sew)
> > > >>> list dydw sew in 1
> > > >>>
> > > >>> predictnl dydl = normalden(`xb')*(_b[len]+ _b[wl]*`meanwei')
> > in 1, se(sel)
> > > >>> list dydl sel in 1
> > > >>>
> > > >>> predictnl dydlw =normalden(`xb')*(-(`xb'))*(_b[weight]+
> > > >>> _b[wl]*`meanlen')*(_b[len]+ _b[wl]*`meanwei') + normalden(`xb')*(
> > > >>> _b[wl]) in 1, se(selw)
> > > >>> list dydlw selw in 1
> > > >>>
> > > >>> Ebru
> > > >>
> > > >> -------------------------------------------
> > > >> Richard Williams, Notre Dame Dept of Sociology
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> > > >> HOME: (574)289-5227
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> > > >>
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> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME: (574)289-5227
> EMAIL: Richard.A.Williams.5@ND.Edu
> WWW: http://www.nd.edu/~rwilliam
>
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