I am trying to perform a multi linear regression model:
$$y_i = β_0 + β_1x_{i1} + β_2x_{i2} +... + β_px_{ip} + ε_i$$
where $$x_{i1}, x_{i2}, ..., x_{ip}$$ are highly correlated with each other (VIFs can be as low as 5 and high as 10).
I am just wondering if there exists a procedure with the following properties:
1) reduces the collinearity of the variables (e.g. VIFs should be lower than 5 after the procedure)
2) the variables after the procedure should maintain the original meanings/interpretations.. (so PCA and FA are out).
3) not dropping any of the variables. I should have all p original varaibles.. (So LASSO and RIDGE are out)