A Maximal Extension of the Gauss-Markov Theorem and Its Nonlinear Version

In this paper, first we make a maximal extension of the well-known Gauss-Markov Theorem (GMT) in its linear framework. In particular, the maximal class of distributions of error term for which the GMT holds is derived. Second, we establish a nonlinear version of the maximal GMT and describe some interesting families of distributions of error term for which the nonlinear GMT holds.

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Year of publication:	2002
Authors:	Kariya, Takeaki ; Kurata, Hiroshi
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 83.2002, 1, p. 37-55
Publisher:	Elsevier
Keywords:	Gauss-Markov theorem nonlinear versions of Gauss-Markov theorem location-equivariant estimator generalized least squares estimator elliptically symmetric distribution

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

Persistent link: https://www.econbiz.de/10005221749