Improved Predictions in Linear Regression Models with Stochastic Linear Constraints

In this article, we have considered two families of predictors for
the simultaneous prediction of actual and average values of study
variable in a linear regression model when a set of stochastic
linear constraints binding the regression coefficients is
available. These families arise from the method of mixed regression
estimation. Performance properties of these families are analyzed
when the objective is to predict values outside the sample and
within the sample.