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From |
Maarten buis <[email protected]> |

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[email protected] |

Subject |
Re: st: Interaction effects with poisson models |

Date |
Wed, 20 Jan 2010 02:32:47 -0800 (PST) |

--- On Wed, 20/1/10, Fabio Zona wrote: > recently, several articles have been published on graphical > tools for testing interaction in regressions with > dichothomous dependent variable (i.e., logit and probit). > They say that simply looking at the significance of the > interaction coefficient is not enough, and that one should > look at graphs to establish whether an interaction effect is > significant. > Is it the same also for poisson and zero-inflated poisson > models? Or - in these latter models - I can simply look at > the significance of the interaction to state that the > interaction effect is significant? The issue is that in these models the effect of an explanatory variable on the predicted probability depends on the values of all other explanatory variables, so the size of the interaction effect *in this probability metric* also depends on the values of all other explanatory variables. So, as long as you want to interpret this effects in terms of the probability metric, there isn't one effect or interaction effect, but as many as there are observations (actually the number of distinct combinations of values on the other covariates). That is where those graphical comparisons come in, that is one way of comunicating that many effects. The obvious alternative solution is to interpret the results on the odds ratio metric, in which case the effect of one variable is separate from the other variables (except for interactions), so there is still one effect and one interaction effect, so you can safely interpret the interaction term(*). Whether you want your effects in terms of the probability differences or odds ratios is a substantive question which I discussed briefly in this post: <http://www.stata.com/ statalist/archive/2010-01/msg00276.html> Pretty much the same argument holds for other non-linear models like -poisson- or -zip-, except that now the choice is between effects in terms of count differences or rate ratios. Hope this helps, Maarten (*) There is another issue with the influence unobserved heterogeneity can have on interaction terms in non-linear models, see for instance: Williams, Richard. 2009. "Using Heterogeneous Choice Models To Compare Logit and Probit Coefficients Across Groups." Sociological Methods and Research, 37(4):531-559. A pre-publication version is available at: <http://www.nd.edu/~rwilliam/oglm/RW_Hetero_Choice.pdf> -------------------------- 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/

**References**:**st: Interaction effects with poisson models***From:*Fabio Zona <[email protected]>

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