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


From   Ebru Ozturk <ebru_0512@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Obtaining marginal effects and their standard errors after estimations with interactions
Date   Sun, 6 Jan 2013 15:11:18 +0200

But without getting separate interaction terms, how do we know that the moderator affects the relationship between x and y positively or negatively?

----------------------------------------
> Date: Sat, 5 Jan 2013 13:39:53 -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
>
> Thanks for the references. FYI, if you have Stata
> 11 or higher, here is how you can easily
> reproduce almost everything that is in the FAQ at
> http://www.stata.com/support/faqs/statistics/marginal-effects-after-interactions/
> -- the one exception being that you DON'T get
> separate marginal effects for the interaction terms.
>
> sysuse auto, clear
> regress mpg weight c.weight#c.weight
> margins, dydx(*) atmeans
> sysuse auto, clear
> replace weight=weight/1000
> replace length=length/10
> probit foreign weight length c.weight#c.length, nolog
> margins, dydx(*) atmeans
> sysuse auto, clear
> set seed 12345
> generate dum=uniform()>0.5
> table dum
> probit foreign turn i.dum i.dum#c.turn, nolog
> margins, dydx(*) atmeans
>
> With regards to the references, Greene is
> brilliant but I wish he would write in English
> and use Stata examples. I think he is saying that
> the marginal effect of the interaction is not
> useful. The other two articles are also
> expressing concerns or suggesting alternatives. I
> am also not a big fan of using MEMs (marginal
> effects at the means); AMEs (Average Marginal
> Effects) make more sense to me, especially when
> categorical variables are involved.
>
> If the marginal effect of the interaction term is
> useful or even valid, I continue to wonder why
> -margins- does not provide it. And what exactly
> does it mean? The interaction term can't change
> independently of the variables used to compute the interaction.
>
> At 11:53 AM 1/5/2013, André Ferreira Coelho wrote:
> >Dear all,
> >
> >As far as i know there is no consensus on whether margins should be
> >computed for marginal terms.
> >
> >Maybe you are interested in using odds for interactions instead of
> >margins.
> >
> >But you might want to take a look on some literature:
> >
> >http://www.maartenbuis.nl/publications/interactions.pdf
> >
> >http://pages.stern.nyu.edu/~wgreene/DiscreteChoice/Readings/Greene-Chapter-23.pdf
> >
> >http://www.stata-journal.com/sjpdf.html?articlenum=st0063
> >
> >Best,
> >
> >Andre
> >
> >
> >
> > > From: ebru_0512@hotmail.com
> > > To: statalist@hsphsun2.harvard.edu
> > > Subject: RE: st: Obtaining marginal effects and their standard errors
> >after estimations with interactions
> > > Date: Sat, 5 Jan 2013 13:32:47 +0200
> > >
> > > 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 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/
> >[1]
> > > > > >
> > > > > > 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/
> >[2]
> > > > > >>>
> > > > > >>> 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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