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I am familiar with the dummy variable trap in normal OLS, in which we should include one less dummy variable than the total of categories to avoid the problem of multicollinearity.

However, I was wondering if it is also the case in duration models. Specifically, I am running a Weibull survival model with six dummy variables, exhaustive and mutually exclusive, and there is no sign of multicollinearity (Even if all the coefficients of the dummy variables are negative, which makes it a bit harder to interpret as I'm not sure what the dummies are measuring themselves against).

So the question is: does the dummy variable trap also occur in survival/duration models?

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    $\begingroup$ Should be the same as in OLS... $\endgroup$
    – user73432
    Commented Apr 13, 2015 at 12:47
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    $\begingroup$ Are you also fitting an intercept? $\endgroup$ Commented Apr 13, 2015 at 15:03
  • $\begingroup$ Yes, there is also an intercept. Also the AIC is (slightly) higher when I exclude one of the dummy variables, which implies that the model fit is better with all the dummies. $\endgroup$
    – Not Fisher
    Commented Apr 13, 2015 at 15:58
  • $\begingroup$ How are you determining that you don't have perfect collinearity in the design matrix? $\endgroup$ Commented Apr 13, 2015 at 16:06
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    $\begingroup$ Very trusting of you! Check the condition number of $X^\mathrm{T}X$ (where $X$ is the design matrix). Or try changing the order in which coefficients are input to the model (I've just experimented with the survfit function in R - fitting a Weibull model when the design matrix isn't full rank also gives one of the infinity of possible solutions without warning; it gives different solutions when you vary the order of the predictors). $\endgroup$ Commented Apr 13, 2015 at 16:39

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Dummy Variable trap usually occurs in proportional hazards models/semi-parametric models very well. and yes! you should check significant improvement of each dummy variable you add.

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  • $\begingroup$ But why can it also occur in these models? And what of the OP's assertion that in their model there isn't perfect collinearity in the model matrix? $\endgroup$ Commented Apr 13, 2015 at 15:06
  • $\begingroup$ I studied these models are good to test/judge by dummy variables & Yes! Semi-parametric is good for such. $\endgroup$ Commented Apr 13, 2015 at 15:14
  • $\begingroup$ As @Scortchi said, there is no multicollinearity in the regression output, so that's why I'm not exactly sure it occurs in survival models... $\endgroup$
    – Not Fisher
    Commented Apr 13, 2015 at 15:56
  • $\begingroup$ You know Cox hazard model. $\endgroup$ Commented Apr 13, 2015 at 16:26
  • $\begingroup$ Very strange second part of the answer. $\endgroup$
    – Michael M
    Commented Jan 3, 2023 at 16:13

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