Double Shrinkage Estimation of Common Coefficients in Two Regression Equations with Heteroscedasticity

Abstract

The problem of estimating the common regression coefficients is addressed in this paper for two regression equations with possibly different error variances. The feasible generalized least squares (FGLS) estimators have been believed to be admissible within the class of unbiased estimators. It is, nevertheless, established that the FGLS estimators are inadmissible in light of minimizing the covariance matrices if the dimension of the common regression coefficients is greater than or equal to three. Double shrinkage unbiased estimators are proposed as possible candidates of improved procedures.common mean problem feasible (two-stage) generalized least squares estimators inadmissibility unbiased estimation heteroscedastic linear regression model