hallo everyone I am working with a panel dataset consisting of 219 observations (73 firms over 3 years). My model uses 5 independent variables (X1–X5) and 1 dependent variable (Y). I estimated the model using fixed effects (FEM).
When I checked multicollinearity using vif, I found that X1 and X5 have VIF values above 10, while the other variables are below the usual thresholds.
To address this, I orthogonalized X1 and X5 (to reduce their correlation with the other predictors). After orthogonalization, VIF values dropped to acceptable levels.
My questions:
Thank you.
When I checked multicollinearity using vif, I found that X1 and X5 have VIF values above 10, while the other variables are below the usual thresholds.
To address this, I orthogonalized X1 and X5 (to reduce their correlation with the other predictors). After orthogonalization, VIF values dropped to acceptable levels.
My questions:
- Is orthogonalizing variables (like X1 and X5) acceptable practice in panel data models estimated via fixed effects?
- After orthogonalization, should I still worry about multicollinearity when interpreting coefficients?
- Is there a better approach to handle multicollinearity in panel data, besides orthogonalization (e.g., centering, dropping variables, or using ridge regression)?
- Does the short time dimension (T=3) affect how VIF behaves in panel settings?
Thank you.
