My question concerns use of VIF test for multicolinearity diagnostics when the model suffers from heteroskedasticity.
I want to use HAC correction to account for heteroskedasticity in my model. However VIF gives my starkly different results depending if I run it after estimating the model with simple OLS without error correction, compared to when I start with regression with HAC applied and then run VIF. I use Eviews.
For me it was surprising as the test statistic in VIF is just an 1/(1-R^2) where R^2 is calculated for a model in which given x_i variable is regressed against the rest of X variables. This implies that the result should not depend on standard errors of the estimated parameters in our original y against X regression, and thus should not depend on whether I use robust errors or not.
However, in Eviews VIF is calculted differently and estimates of standard errors for parameters are used (tutorial, pp. 198 of the pdf). While it is suggested that both approaches are equivalent, clearly is not the case in my example.
In short, in which order should I proceed - first test for multicolinearity with simple OLS model and then move on to model with HAC, or the other way - estimate the model with HAC and then run VIF? Thanks for all your help!
