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From |
Julian Runge <rungejuq@cms.hu-berlin.de> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Wed, 13 Jun 2012 16:50:06 +0200 |

Hello! Two brief (closely related) questions that I could not find a definite answer to yet, neither in the literature nor in the discussion with peers. I would really appreciate your input, especially on question 1: 1) My model has a categorical dependent variable and all independent variables are binary. I used a multinomial logit model with y={0, 1, 2} and 0 as base outcome to estimate the model. After running the regression, I applied the following commands to get marginal effects: margins, predict(outcome(1)) dydx( x1 x2 ... ) atmeans margins, predict(outcome(2)) dydx( x1 x2 ... ) atmeans Now I am unsure how to interpret the marginal effects. I would do as follows: It is the ceteris paribus mean effect for a discrete change in the respective binary independent variable from zero to one for a representative individual (in terms of “being average" on all variables, i.e. the covariates are fixed at their mean) in the sample. Let us consider an example to make this more accessible: The marginal effect on x1 for category y=1 tells us that, ceteris paribus, a subject that answers “yes” (x1=1) instead of “no” (x1=0) has a 0.0a (a%) higher probability to be part of category y=1. --> Am I getting this right? 2) A credible online source noted the following: "The default behavior of margins is to calculate average marginal effects rather than marginal effects at the average or at some other point in the space of regressors." Taking this into account I would think that I am calculating an "average marginal effect at the average" above. Is that correct? Thank you in advance, Julian * * 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: Interpreting marginal effects for binary variables in multinomial logit***From:*Austin Nichols <austinnichols@gmail.com>

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