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

 From Ebru Ozturk To Subject RE: st: Obtaining marginal effects and their standard errors after estimations with interactions Date Fri, 4 Jan 2013 18:31:13 +0200

```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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>
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