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I have a linear regression model that has no multicolinearity problem with low VIF scores. However, when I include the interaction term, this interaction term and its components get very high VIF scores. Can I ignore the multicolinearity problem and high VIF scores of the interaction term in this case?

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The short answer is yes. Interaction terms tend to be collinear with the original variables involved. That is why post-hoc interaction tests are often underpowered.

Interaction that is unaccounted for renders the estimate wrong, while inflated variance inflates p-value. If the interaction terms are already statistically significant, inflation of variance is no longer a problem.

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  • $\begingroup$ Thank you very much for your reply. Then reversely, if the interaction terms are not statistically significant, the VIF is a problem? $\endgroup$ – Eric Apr 18 '17 at 17:55
  • $\begingroup$ If the interaction terms are not significant then the reduced model with the interaction term is usually preferred. For that reason VIF became irrelevant. $\endgroup$ – Penguin_Knight Apr 19 '17 at 17:09

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