Ordinary least squares and Stein-rule predictions in regression models under inclusion of some superfluous variables

This article considers a misspecified linear regression model in which misspecification relates to the inclusion of some explanatory variables. Assuming the distribution of disturbances to be not necessarily normal, this paper investigates the efficiency properties of predictions arising from ordinary least squares and Stein-rule when the aim is to predict either the actual value or the mean value of the study variable.