HELP my intercept becomes significant

Hi all,

I run 12 regressions on the bid premium in a Event Study. For the first 7 regressions, when adding the explanatory variables (after the control variables for the first regression), the constant or intercept stays insignificant and explains less after adding the variables. But from regression 7 forward, when adding variables which have a significant effect on the bid premium, the constant becomes significant as well.

This feels very contra intuitive since the intercept (or constant) is the mean of the response when all predictors are zero.

Does anyone has a logical explanation for this?? Or an article I could refer to??? One can find the snapshot in the attached file.

Would mean the world to me.


Can you elaborate on that? For me personal, it would be great if I can shortly state something about my regressions specifically or refer to an article. Since it feels counterintuitive that the intercept becomes larger after adding significant explanatory variables which are completely robust (no hetroskedacticity, multicolliniarity etc.)


Ambassador to the humans
The thing is that if it doesn't make sense for all of your predictors to be zero simultaneously then interpreting the intercept is essentially extrapolating. A linear fit might make sense (or at least be an adequate approximation) for the space that your predictors reside in but that doesn't mean that the fit will make sense outside of that space without additional assumptions.