Properties of shrinkage estimators in linear regression when disturbances are not normal

This paper considers a class of recently developed biased estimators of regression coefficients and studies its sampling properties when the disturbances are not normally distributed. It has been found that the conditions of dominance of these estimators over the least squares estimator, under non-normality, are quite different than their well-known dominance conditions under normality. Some implications of the results are also discussed.