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| From | Arne Risa Hole <arnehole@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Obtaining marginal effects and their standard errors after estimations with interactions |
| Date | Mon, 7 Jan 2013 15:46:22 +0000 |
I don't think it can be extended to AMEs, since the -at()- option of -margins- only takes a single number per variable as far as I know. I imagine you could extend it to three-way interactions etc., but I have not tried this. It's likely to get pretty messy. On 7 January 2013 15:26, Richard Williams <richardwilliams.ndu@gmail.com> wrote: > Thanks again. I wonder how easy it would be to extend this to AMEs, three > way interactions involving squared terms, etc. In other words, would it be > relatively simple for margins to provide marginal effects for more > complicated models with interaction terms, or would it be quite difficult? > And if you could do it, how useful would it be? > > > At 10:08 AM 1/7/2013, Arne Risa Hole wrote: >> >> It's possible to get pretty close when both variables are continuous >> as well, using a finite difference approximation. The code below >> approximates "dydlw" in the FAQ. >> >> sysuse auto, clear >> replace weight=weight/1000 >> replace length=length/10 >> probit foreign weight length c.weight#c.length, nolog >> margins, dydx(*) atmeans at(weight=3.019559) >> matrix b = r(b) >> scalar meff_turn_1 = b[1,2] >> margins, dydx(*) atmeans at(weight=3.019459) >> matrix b = r(b) >> scalar meff_turn_0 = b[1,2] >> >> di (meff_turn_1 - meff_turn_0)/0.0001 >> >> I'm not saying that this is a good way of actually performing these >> calculations, but it does help in giving some intuition to what the >> numbers represent (at least I think so). >> >> Arne >> >> On 7 January 2013 13:41, Arne Risa Hole <arnehole@gmail.com> wrote: >> > I don't have much to add to what Richard has already said on this >> > topic but I just wanted to mention one thing: if the interaction is >> > between a continuous variable and a dummy variable, then the second >> > derivative (or "marginal effect of the interaction") is the difference >> > between the marginal effect of the continuous variable when the dummy >> > is "switched on" and when dummy is "switched off". The code below >> > replicates the final result in the FAQ (this uses -margins- so >> > requires Stata 11 or higher). >> > >> > 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 at(dum=1) >> > matrix b = r(b) >> > scalar meff_turn_dum1 = b[1,1] >> > margins, dydx(*) atmeans at(dum=0) >> > matrix b = r(b) >> > scalar meff_turn_dum0 = b[1,1] >> > >> > di meff_turn_dum1 - meff_turn_dum0 >> > >> > Arne >> > >> > On 6 January 2013 13:11, Ebru Ozturk <ebru_0512@hotmail.com> wrote: >> >> 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/Discret >> >>> > eChoice/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/statistic >> >>> > s/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/statistic >> >>> > s/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 >> >>> >> >>> >> >>> * >> >>> * For searches and help try: >> >>> * http://www.stata.com/help.cgi?search >> >>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> >>> * http://www.ats.ucla.edu/stat/stata/ >> >> * >> >> * For searches and help try: >> >> * http://www.stata.com/help.cgi?search >> >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> >> * http://www.ats.ucla.edu/stat/stata/ >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ > > > ------------------------------------------- > 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 > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/