Financial Risk Manager Part 1
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What is the implication of a variable having a large variance inflation factor (VIF) in a model?
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NNikitesh
A
There is a high correlation between the variable and other remaining variables, and thus the model should be improved.
B
The variables in the model are uncorrelated and thus appropriate in explaining the dependent variable
C
Having a variable with large VIF implies that we need to include more explanatory variables in the model
D
None of the above
Explanation:
The correct answer is A. A large Variance Inflation Factor (VIF) indicates multicollinearity, meaning there is high correlation between the variable and other explanatory variables in the model. This causes several problems:
- Poor coefficient estimation: The coefficients become unstable and have high standard errors
- Reduced statistical significance: Even if variables are important, they may appear statistically insignificant
- Difficulty in interpretation: It becomes hard to isolate the individual effect of each variable
Why other options are incorrect:
- Option B: Contradicts the definition of VIF - large VIF indicates correlation, not uncorrelated variables
- Option C: Adding more variables would likely worsen multicollinearity, not improve it
- Option D: Incorrect because option A is correct
Practical implications: When VIF > 10 (or sometimes > 5), it suggests problematic multicollinearity. Solutions include:
- Removing the highly correlated variable
- Combining correlated variables
- Using regularization techniques (ridge regression)
- Collecting more data
- Using principal component analysis
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