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From |
David Hoaglin <[email protected]> |

To |
[email protected] |

Subject |
Re: st: nbreg - problem with constant? |

Date |
Fri, 2 Mar 2012 16:54:18 -0500 |

Hi, Simon. When you remove the constant from the model, some of the variation in the dependent variable the was accounted for by the constant is then accounted for (to the extent possible) by the predictors that remain in the model. The result is not necessarily to make the coefficients of those predictors larger, but they will generally change. Consider how removing the constant would work in ordinary multiple regression. If a predictor variable does not have mean 0 (in the data), removing the constant will change its coefficient. You can even see this happen in simple regression when you fit a line that has a slope and an intercept and then fit a line through the origin. It's easy to construct an example in which the two slopes have different signs. One has to keep in mind that the definition of a coefficient in a regression (or similar) model depends on the list of other predictors in the model. In your negative binomial model, I don't think you want to take exp of the coefficients and interpret the results as if they were the coefficients. Regards, David Hoaglin * * 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: nbreg - problem with constant?***From:*"Jacobs, David" <[email protected]>

**References**:**st: nbreg - problem with constant?***From:*Simon Falck <[email protected]>

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