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

[Richard Williams <richardwilliams.ndu@gmail.com>]

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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