Least upper bound for the covariance matrix of a generalized least squares estimator in regression with applications to a seemingly unrelated regression model and a heteroscedastic model
Hiroshi Kurata and Takeaki Kariya
Source: Ann. Statist.
Volume 24, Number 4
(1996), 1547-1559.
Abstract
In a general normal regression model, this paper first derives the least upper bound (LUB) for the covariance matrix of a generalized least squares estimator (GLSE) relative to the covariance matrix of the Gauss-Markov estimator. Second the result is applied to the (unrestricted) Zellner estimator in an N-equation seemingly unrelated regression (SUR) model and to the GLSE in a heteroscedastic model.
Primary Subjects: 62J05
Secondary Subjects: 62M10
Keywords: Nonlinear Gauss-Markov theorem; efficiency of GLSE; seemingly unrelated equation; heteroscedastic model; Kantorovich inequality
Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.aos/1032298283
Mathematical Reviews number (MathSciNet):
MR1416648
Digital Object Identifier: doi:10.1214/aos/1032298283
Zentralblatt MATH identifier:
0868.62060
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KUNITACHI, TOKy O, 186 YAMAGUCHI-SHI, YAMAGUCHI-KEN JAPAN JAPAN E-MAIL: cr00055@srv.cc.hit-u.ac.jp
