Linear restrictions and two step least squares with applications

Abstract

In this paper we consider the full rank regression model with arbitrary covariance matrix: Y = Xß + ε. It is shown that the effect of restricting the information Y to T = A′ Y may be analyzed through an associatedi regression problem which is amenable to solution by two step least squares. The results are applied to the important case of missing observations, where some classical results are rederived.