Linear restrictions and two step least squares with applications

Guido E. del Pino

Statistics & Probability Letters, 1984, vol. 2, issue 4, 245-248

Abstract: In this paper we consider the full rank regression model with arbitrary covariance matrix: Y = Xß + [var epsilon]. 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.

Keywords: linear; models; two; step; least; squares; influential; data; missing; data; dummy; variables (search for similar items in EconPapers)
Date: 1984
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