TITLE:
Improving the Ordinary Least Squares Estimator by Ridge Regression
AUTHORS:
Ghadban Khalaf
KEYWORDS:
OLS Estimator, Multicollinearity, Ridge Regression, Simulation
JOURNAL NAME:
Open Access Library Journal,
Vol.9 No.5,
May
27,
2022
ABSTRACT: In the presence of multicollinearity, ridge regression techniques result in estimated coefficients that are biased but have smaller variance than Ordinary Least Squares estimators and may, therefore, have a smaller Mean Squares Error (MSE). The ridge solution is to supplement the data by stochastically shrinking the estimates toward zero. In this study, we propose a new estimator to reduce the effect of multicollinearity and improve the estimation. We show by a simulation study that the MSE of the suggested estimator is lower than other estimators of the ridge and the OLS estimators.