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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Average marginal effects in ordered probit models |

Date |
Thu, 13 Sep 2012 11:15:37 +0200 |

On Thu, Sep 13, 2012 at 9:31 AM, Chris Ambrey wrote: <snip>. However, I want to use predictnl and/or nlcom > to obtain the average marginal effects for several interaction terms in the > one model. I'm estimating an ordered probit model. I'm using version 11.2 of > STATA. Life satisfaction is my dependent variable (0-10) and I have > greenspace (continuous, percentage of greenspace in the census tract) and a > number of socio-economic and demographic characteristics as explanatory > variables (some dichotomous and others continuous). <snip> > Furthermore, I have found a very recent article "Interaction Terms in > Nonlinear Models" by Karaca-Mandic, Norton and Dowd (2012) which has been > very helpful. On Statalist we ask you to provide full references. See: <http://www.stata.com/support/faqs/resources/statalist-faq/#others> > Additionally, do the average marginal effects apply to a particular outcome, > such as a self-reported life satisfaction score of 10 and are thus > interpreted as the probability of reporting say a 10 for a one unit increase > in the explanatory variable? To start with your last question: Yes. So, by estimating marginal effects you have undone the very advantage of ordered regression. The point of ordered regression is that you can use the ordered nature of the dependent variable to get one effect for each (or at least some of the) explanatory/independent/right-hand-side/x-variable. This simplifies the model a lot, and simplifying is what a model is supposed to do but it also involves a risk in the sense that the constraint could be wrong. If you undo the advantage of an ordered model, by presenting the results in such a way that you need to present effects for each category in the explained/dependent/left-hand-side/y-variable, than there is not much point in incurring the risk without the advantage, and you can just as well do a multinomial logit/probit. However, I would not give up so easily. Instead I recommend you use an ordered logit and interpret the results in terms of odds ratios. You can find an explanation on how to do that in <http://www.stata.com/bookstore/regression-models-categorical-dependent-variables/>. Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin 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/

**Follow-Ups**:**RE: st: Average marginal effects in ordered probit models***From:*"Chris Ambrey" <chris.ambrey@gmail.com>

**References**:**st: Average marginal effects in ordered probit models***From:*"Chris Ambrey" <chris.ambrey@gmail.com>

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