Ridge Regression: An Alternative Technique for Correcting Multicollinearity in Multiple Regression Analysis

Abstract

This study evaluates ridge regression as a robust corrective technique for addressing severe Multicollinearity in multiple regression models using Nigerian macroeconomic data. Using economic data from the Central Bank of Nigeria (CBN) statistical bulletin containing sixteen variables measured in billion naira, we first identified substantial Multicollinearity through diagnostic measures: eigenvalues approaching zero, variance inflation factors (VIFs) exceeding 30 for ten variables, and condition indices surpassing 100 for six variables. The ordinary least squares (OLS) estimates exhibited instability with inflated standard errors. Applying ridge regression with an optimal shrinkage parameter (k = 0.381) effectively mitigated Multicollinearity, reducing all VIFs below 10 and stabilizing coefficient estimates. While introducing controlled bias, the method significantly decreased estimator variance, yielding more reliable and interpretable results. The ridge model maintained a high explanatory power (R² = 0.9421).  The findings demonstrate that ridge regression provides a practical and statistically efficient alternative to ordinary least squares (OLS) when Multicollinearity is severe and variable elimination is undesirable in econometric modeling.