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August 1996 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, Takeaki Kariya
Ann. Statist. 24(4): 1547-1559 (August 1996). DOI: 10.1214/aos/1032298283

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.

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Hiroshi Kurata. Takeaki Kariya. "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." Ann. Statist. 24 (4) 1547 - 1559, August 1996. https://doi.org/10.1214/aos/1032298283

Information

Published: August 1996
First available in Project Euclid: 17 September 2002

zbMATH: 0868.62060
MathSciNet: MR1416648
Digital Object Identifier: 10.1214/aos/1032298283

Subjects:
Primary: 62J05
Secondary: 62M10

Rights: Copyright © 1996 Institute of Mathematical Statistics

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Vol.24 • No. 4 • August 1996
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