Thread by @jmwooldridge

If you know people who teach students it's important to "test" for multicollinearity, please ask them why.

I imagine a world where the phrase "I tested for multicollinearity" no longer appears in published work. I know John Lennon would be on my side.

#metricstotheface

What I'm getting at is that it's still common to see "tests" for multicollinearity without even looking at the regression output. Or asking which variables are collinear. Often it's control variables. So what? If you have many control variables you might have to select.

And a VIF of 9.99 is okay but 10.01 is a disaster? We can do better than this across all fields.

I just saw a post where X1 and X2 have a correlation of .7, and the researcher wonders which variable to drop.

The coefs on X1 and X2 make sense but the coef on X2 flips sign when X1 is dropped. Many doing serious data analysis are still learning about the basic idea of ceteris paribus. In interesting applications, the Xj are often highly correlated.

In the application, it's clear both X1 and X2 should be included, but the researcher has somehow learned that "highly" correlated variables can't appear in the same model. We must do better in our teaching (and I must do better with my writing).

Starting to feel a little sorry for my first-year PhD students, who will be getting the full multicollinearity/VIF rant in the next two weeks. 😈