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From | Maarten Buis <maartenlbuis@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: does margins command obviate concerns that inteff solves? |
Date | Fri, 9 Dec 2011 18:01:34 +0100 |
On Fri, Dec 9, 2011 at 5:39 PM, Doug Hess <douglasrhess@gmail.com> wrote: > Thanks, Maarten. My question, perhaps, is due to terminology. By > adjusted predictions I mean I want to tell the story using percents. > In my case, the percent of households that are food insecure (binary > outcome). To me that is marginal effects. > > For instance: > > Model 1: the effect on the outcome of household race, income, and education > Model 2: the effect on the outcome household race, income, education, > and educationX race > > If I want to find the significance of interaction using -margins, > contrast-, do I need to also use inteff? If you want a story in terms of risk differences than you should use a model that is based on risk differences, i.e. the linear probability model (-regress- with the -vce(robust)- option). If that model does not fit to your data (e.g. predictions outside the range 0-1(00)), than risk differences/marginal effects just aren't an appropriate metric for your problem. You are not going to solve that by estimating a -logit- or -probit- and than report marginal effects. If you report one (average) marginal effect per parameter than you have basically fitted a linear model on top of your non-linear model and reported the coefficients of your linear super model. So now you are back at the linear model that did not fit. -- Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/