Financial Risk Manager Part 1
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Jessica Pearson, FRM, builds a model to study the annual salaries of individuals in a certain developed country. The model incorporates just 2 independent variables - age and experience. She is surprised for ending up with a negative value for the coefficient of experience, which seems to be somewhat counterintuitive. Furthermore, the coefficients have low t-statistics but otherwise the model has a high R². Which of the following is the most likely cause of such results?
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NNikitesh
A
Heteroskedasticity
B
Multicollinearity
C
Homoskedasticity
D
Serial correlation
Explanation:
Multicollinearity exists when two or more of the independent variables, (or their linear combinations) in a multiple regression model are highly correlated with each other. Such a scenario distorts the standard error of the regression as well as the standard errors for the coefficients which might cause problems when carrying out tests for significance.
In this particular case, age and experience are most likely highly correlated. Even if R² is high, multicollinearity will still be present as long as the standard errors for the coefficients are high.
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