It is well-known that one of the major consequences of multicollinearity on the ordinary least (OLS) estimator is that the estimator produces large sampling variances, which in turn might inappropriately lead to exclusion of otherwise significant coefficients from the model. To circumvent this problem, two accepted estimation procedures which are often suggested are the James-Stein method and the ridge regression method. Both of the two methods ensure a smaller mean square error (MSE) value for the estimator in the presence of multicollinearity. In this paper, we have proposed a new estimator and it has been shown, view simulation, that this estimator is superior to both the James-Stein as well as the ordinary ridge regression estimators by the criterion of MSE of the estimator of the regression coefficients.

OLS estimator, multicollinearity, ridge regression, James-Stein estimator, simulation.