Linear restrictions and two step least squares with applications

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.

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Year of publication:	1984
Authors:	Pino, Guido E. del
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 2.1984, 4, p. 245-248
Publisher:	Elsevier
Keywords:	linear models two step least squares influential data missing data dummy variables

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005258995