Small sample properties of ridge estimators with normal and non-normal disturbances

In this paper we consider five well known and widely used ridge estimators when the convenient assumption of normality of the disturbances is abandoned and report on a Monte Carlo study of their small sample properties. The Monte Carlo experiment is applied to four different data sets with artificially varied degrees of multicollinearity, while the disturbances follow normal, lognormal, uniform and Laplace distributions with small and large variances. The results show that the best estimates are obtained for all ridge estimators when the disturbances follow the lognormal distribution. Also, none of the examined ridge estimators shows a consistent behavior under the different settings considered.