What are the key assumptions of multiple regression and how do I detect violations?
I'm working through the regression section of CFA Level I Quant. The curriculum lists assumptions like linearity, homoscedasticity, and no multicollinearity — but I find it overwhelming. What are the most testable assumptions, and how would I spot violations in a vignette?
Multiple regression is a CFA Level I favorite, and the exam almost always tests whether you can identify assumption violations from given data. Here are the key assumptions and how to spot trouble:
The Core Assumptions
- Linearity — The relationship between each independent variable and the dependent variable is linear.
- Homoscedasticity — Error terms have constant variance across all levels of the independent variables.
- No Serial Correlation — Error terms are independent of each other (especially relevant for time-series data).
- No Multicollinearity — Independent variables are not highly correlated with each other.
- Normality of Errors — Error terms are normally distributed (needed for valid hypothesis tests).
How to Detect Violations in a CFA Vignette
| Violation | Detection Method | Red Flag in Data |
|---|---|---|
| Heteroscedasticity | Breusch-Pagan test | Residuals fan out as fitted values increase |
| Serial Correlation | Durbin-Watson statistic | DW far from 2.0 (close to 0 or 4) |
| Multicollinearity | Variance Inflation Factor (VIF) | VIF > 5 or 10; high R² but insignificant coefficients |
Worked Example: An analyst at Cornerstone Research regresses quarterly fund returns (Y) on interest rate changes (X₁) and credit spread changes (X₂). Results:
- R² = 0.81
- t-stat for X₁ = 1.12 (insignificant)
- t-stat for X₂ = 0.97 (insignificant)
- Correlation between X₁ and X₂ = 0.93
The red flag: High R² with insignificant individual coefficients and high correlation between regressors — classic multicollinearity. The model explains returns well overall but can't disentangle the individual effects.
Fix: Drop one variable, combine them into a composite, or use principal component analysis.
Exam Strategy: When you see a regression output in a vignette, immediately check:
- Is R² high but individual t-stats low? → Multicollinearity
- Is the Durbin-Watson far from 2? → Serial correlation
- Do residuals show a pattern? → Heteroscedasticity or non-linearity
Practice identifying these patterns with our CFA Level I question bank.
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