My data is cross-sectional macroeconomics data. I have six independent variables (x1,x2,x3,x4,x5,x6) plus 2 dummies (d1,d2) plus 2 interactions terms (d1*x1,d2*x1).I am testing my data for multicollinearity using VIF and condition indices(CI)
The t-test : seven statistical significant variables F: statistical significant overall
Mean VIF : 10.63 (with very high R-square (>85%) in all dummies and interaction terms) CI : 48.3
When I remove dummies and interactions from the model the results are much more better (Mean VIF : 1.62 , CI: 19.34 R-square <50%).
I am expecting -due to the nature of dummies and interaction terms- that my results would present multicollinearity.
Are the above results serious evidence for multicollinearity in my model?