Page 29 - FINAL CFA II SLIDES JUNE 2019 DAY 3

EXAMPLE: Detecting multicollinearity: Bob Watson, READING 8: MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
CFA, runs a regression of mutual fund returns on average
P/B, average P/E, and average market capitalization, with
the following results @ 10% SL: MODULE 8.8: MULTICOLLINEARITY

Quite high –likely to be in
t-test not provided! rejection zone: How do we
know this?

2
R is high The three variables as a group do an excellent job
of explaining the variation in mutual fund returns.
Is multicollinearity a problem in this regression?
Fulfills 2 of 3 (t not available, so 2/2)
How do we detect multicollinearity?
Classic indication of multicollinearity.
2
Check if t-tests says Fail to Reject Ho; whilst F says Reject Ho whilst the R is high.
(This is telling us the independent variables may have a common source of variation
which is explaining the dependent variable, but the high degree of correlation also
“washes out” the individual effects (hence contradictory t and f test results).