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Christina
Christina

Reputation: 255

Which statistic to use when testing for multicollinearity in python?

I've been reading a lot about multicollinearity but am still unsure whether to use the Durbin-Watson score, the eigenvalues or the variance inflation factor. I only have three independent variables and the eigenvalues are:

1.81768828 0.95241948 0.22989225

How I understood it, only values too close to zero indicate multicollinearity. I wasn't sure if the last one (0.22) counts as "close to zero" but when checking its eigenvector, the result is:

-0.53977799 -0.44013805 0.71757802

and each of them would indicate collinearity as they are NOT close to zero (this time it's the other way around). Am I correct until here?

The Durbin-Watson score is 1.93 (calculated through the summary() function from statsmodels with an added intercept). This does NOT show strong multicollinearity, right?

As nobody gives clear "cutoff" values, I am a bit confusing as to which values count as "close to zero" or not.

Should I calculate the VIF as well, just to be extra sure?

Any help is much appreciated!

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