Hi,

I am working a logit model with a binary dependent variable. Some of the independent variables I am using are continuous, and from some preliminary graphical analysis (and theory) I have reasons to believe that they have a non-linear relationship with my dependent variable. However, when I try to add squared terms for one of these variables, my model does not achieve convergence. I have found that one way around this is to drop extreme values of the problematic independent variable. While the model achieves convergence with the squared term after this, I am not sure this is the appropriate way of dealing with the problem (I have no way of knowing if these extreme values are errors in the data or not, and therefore no valid reason to drop them). Does anyone have some advice? Should I abandon taking non-linearities into account because my model does not converge when they are added? Are there other ways around this?

Thanks