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RE: st: Obtaining marginal effects and their standard errors after estimations with interactions
From
Richard Williams <[email protected]>
To
[email protected], <[email protected]>
Subject
RE: st: Obtaining marginal effects and their standard errors after estimations with interactions
Date
Sat, 05 Jan 2013 13:39:53 -0500
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: [email protected]
> To: [email protected]
> 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: [email protected]; [email protected]
> From: [email protected]
> 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: [email protected]
> > > Date: Fri, 4 Jan 2013 11:56:50 -0500
> > > Subject: Re: st: Obtaining marginal effects and their standard
> > errors after estimations with interactions
> > > To: [email protected]
> > >
> > > 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: [email protected];
[email protected]
> > > >> From: [email protected]
> > > >> 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: [email protected]
WWW: http://www.nd.edu/~rwilliam
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