Enhanced ridge estimators to effectively address multicollinearity challenges

In this study, we introduce three new ridge estimators, referred to as AIRE1, AIRE2, and AIRE3, designed to effectively address the issue of multicollinearity in regression analysis. The performance of these estimators was compared with the Ordinary Least Squares (OLS) and existing ridge estimators using the Mean Squared Error (MSE) as a benchmark. Theoretical comparisons of estimators are not possible. We used Monte Carlo simulations to assess their performance based on MSE. Although no single estimator works best across all scenarios of multicollinearity, the findings from simulations and real-world datasets demonstrated that the proposed estimators outperformed OLS and other ridge estimators. Among all the estimators, the newly proposed AIRE1 consistently outperformed the rest, establishing itself as a reliable and effective solution for mitigating multicollinearity.