Standardize one predictor variable or all predictor variables to solve multi-collinearity I was using a fixed-effects panel model with interaction effects when I realized that the VIF values are too high for some variables. I was advised to standardize the predictor variables to mitigate multi-collinearity. My question is that can I standardize just 1 predictor variable or must I standardize all predictor variables?
If so, are there any academic sources/literatures that I could refer to for this matter?

In this excellent answer the author goes into the derivation of the covariance between X and the interaction term XY. He shows that when there is total independence of X and Y $Cov(X,XY)=\sigma^2_x \mu_y$. Standardizing Y alone causes $\mu_y=0$ and thus reduces the covariance in this case to zero. I don't know how to evaluate the case for when $Cov(X,Y)!=0$. But at the bottom the author gives some intuition, about how XY will be big if X is big and thus they will be correlated. This suggests we would like X and Y both to be standardized, because either one of the variables could induce a correlation between the variable and XY.
