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


From   Richard Williams <richardwilliams.ndu@gmail.com>
To   statalist@hsphsun2.harvard.edu, <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Obtaining marginal effects and their standard errors after estimations with interactions
Date   Fri, 04 Jan 2013 15:15:58 -0500

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
> >> 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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-------------------------------------------
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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