Multicollinearity in Multiple Regression Analysis

Very Hard Multiple Regression Analysis Multicollinearity

Consider a multiple regression model estimated to understand the impact of various independent variables on the dependent variable, which is the return on a specific stock. The model includes three independent variables: Market Return (X₁), Company Size (X₂), and Debt-to-Equity Ratio (X₃). Upon running the regression analysis, the following diagnostics were observed:

The Variance Inflation Factor (VIF) for Market Return is 1.5, for Company Size is 10.2, and for Debt-to-Equity Ratio is 9.5.
The condition number of the model is 32, signifying a potential multicollinearity issue.

Based on these results, which of the following statements regarding multicollinearity in this regression model is correct?

The model exhibits serious multicollinearity issues, indicating that the coefficients for Company Size and Debt-to-Equity Ratio should be interpreted with caution.

The VIF values suggest that there are no multicollinearity concerns as the maximum VIF is below 15.

The condition number indicates that multicollinearity is extremely high, necessitating the removal of at least one independent variable.

Hint

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