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
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.
Publié le : 1996-08-14
Classification:
Nonlinear Gauss-Markov theorem,
efficiency of GLSE,
seemingly unrelated equation,
heteroscedastic model,
Kantorovich inequality,
62J05,
62M10
@article{1032298283,
author = {Kurata, Hiroshi and Kariya, Takeaki},
title = {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},
journal = {Ann. Statist.},
volume = {24},
number = {6},
year = {1996},
pages = { 1547-1559},
language = {en},
url = {http://dml.mathdoc.fr/item/1032298283}
}
Kurata, Hiroshi; Kariya, Takeaki. 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., Tome 24 (1996) no. 6, pp. 1547-1559. http://gdmltest.u-ga.fr/item/1032298283/