Performance of the stein-rule estimators when the disturbances are misspecified as homoscedastic

The paper investigates the effects of misspecifying the disturbances in a linear regression model as homoscedastic on the efficiency properties of the Stein-rule estimators. Asymptotic distribution of the Stein-rule estimator based on the OLS estimator is derived when the disturbances covariance matrix is nonscalar. The effects of non-homoscedasticity of the disturbances on the dominance conditions of the Stein-rule estimator is also observed. The risks under quadratic loss function of the Stein-rule estimators based on the OLS and the FGLS estimators are compared under a Pitman drift criterion.