Using Ridge Regression to disentangle highly correlated explanatory variables

Folks,

Need your advice and any practical solutions.

We recently conducted a retrospective regression analysis where 3
variables were highly correlated (high VIFs). Decided to use a
principal components approach to create a factor score for input into
the regression model, which did it's job at reducing the VIF greatly.

However, the three highly correlated variables were each of great
interest. A colleague suggested using Ridge Regression to disentangle
the relative impact of each of the three explanatory variables. This
did show that one of the three variables was much more impactful.

Now I'm left wondering if this makes sense, given they were so highly
correlated to begin with. Wouldn't we conclude that they are all
equally contributing - i..e, the factor loading can be divided in
terms of relative impact equally among the three variables?

What's your opinion on this type of issue. I need some practical
advice, point of view, and/or alternate approach to consider.
Remember that the three variables are each of particular interest, so
need to somehow cull out their relative impact.

Very much appreciate any and all help. Thanks!

John
.

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