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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Interpreting marginal effects for binary variables in multinomial logit |

Date |
Thu, 14 Jun 2012 07:52:51 -0400 |

Julian, Whether ceteris paribus can apply to your model depends on how the data cover the various combinations of the binary independent variables. If you want to examine the effect of changing one of those independent variables while holding the other independent variables constant, you need to do it at combinations of those variables where you have data for both values of the variable that you're changing. It comes down to examining the predicted values across the whole set of combinations of the independent variables (in your data). For various types of regression models, the common interpretation (found, unfortunately, in many textbooks) that holds the other independent variables constant is generally an oversimplification. What regression actually does is adjust for the contributions of the other independent variables. It may not be possible to hold them all constant or, depending on the data, to hold them constant in any meaningful place (as Austin pointed out). David Hoaglin On Thu, Jun 14, 2012 at 7:19 AM, Julian Runge <rungejuq@cms.hu-berlin.de> wrote: > Thanks for your comments. I agree on the "awkwardness" of fixing > binary covariates at the mean. > * * 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/

**References**:**st: Interpreting marginal effects for binary variables in multinomial logit***From:*Julian Runge <rungejuq@cms.hu-berlin.de>

**Re: st: Interpreting marginal effects for binary variables in multinomial logit***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Interpreting marginal effects for binary variables in multinomial logit***From:*Julian Runge <rungejuq@cms.hu-berlin.de>

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