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So far I have checked the tolerance value, VIF and condition indexes. But checking the variance of the regression coefficients I have to wonder: how little variance of the regression coefficient should be associated with the smallest eigenvalue and what is too much (indicating multicollinearity)?

Jennifer
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The rule of thumb I've often read is VIF > 5 indicates a level of multicollinearity worth investigating (and Tolerance is just the reciprocal of VIF).

RobertF
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  • Yes, I've read similar information about the VIF. So if the VIF value doesn't give a reason for concern I don't have to check the other values like condition index? – Jennifer Feb 20 '14 at 06:59
  • No I'd look at both VIF and condition index - also see this similar question: http://stats.stackexchange.com/questions/4099/vif-condition-index-and-eigenvalues. Keep in mind if you're building a predicitve model, a little multicollinearity isn't necessarily bad since coefficient estimates are unbiased even if SEs are inflated. – RobertF Feb 20 '14 at 17:52