Hi Forum,

I just came across this question in the quant section:

Hamilton’s conclusion that multicollinearity is not a problem, is *most likely* based on the observation that:

- model
*F*-value is high and the*p*-values for the S&P 500 and SPREAD are low. - correlation between the S&P 500 and SPREAD is low.
- model
*R*^{2}is relatively low.

Correct answer would have been (2) because correlation b/w S&P and Spread is low (given in the question).

However I chose (3) because the question also shows a R^{2} of only 0.4. As far as I know, testing for multicollinearity is the following:

- high R
^{2}or high F-stat together with insignificant t-stat indicate multicollinearity.

So this would be the case here as well!?

Can someone please have a quick look? Many thanks and kind regards

Update: Basically, what is a “high” or “low” R^{2}? Here the model explains 40% of the independent variables…