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 R2 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 R2 of only 0.4. As far as I know, testing for multicollinearity is the following:
- high R2 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” R2? Here the model explains 40% of the independent variables…