I am conducting an analysis, where I examine the size of the intercepts of three regression models (time-series). The models look something like this:
When I run the regressions, I then examine values of $\alpha$ and $t(\alpha)$ for all three models. I find that as I add factors (going form model 1 to 3), the $R^2$ increases (as expected), so it seems that the added factors add some explanation. However, I find that the $t(\alpha)$ values increase as I add factors.
Maybe this is something that I am missing, but shouldn't $t(\alpha)$ decrease as I add factors, since more of the explanatory output is now explained by the factors, and is not put on the intercept?
Edit: $t(\alpha)$ is the estimated $t-$statistic of the $\alpha$ intercept from the regression output.