Seemingly unrelated regression model with unequal size observations: computational aspects

Foschi, P. and Kontoghiorghes, Erricos (2002) Seemingly unrelated regression model with unequal size observations: computational aspects. Computational Statistics & Data Analysis 41 (1), pp. 211-229. ISSN 0167-9473.

Full text not available from this repository.

Official URL: https://doi.org/10.1016/S0167-9473(02)00146-9

Abstract

The computational solution of the seemingly unrelated regression model with unequal size observations is considered. Two algorithms to solve the model when treated as a generalized linear least-squares problem are proposed. The algorithms have as a basic tool the generalized QR decomposition (GQRD) and efficiently exploit the block-sparse structure of the matrices. One of the algorithms reduces the computational burden of the estimation procedure by not computing explicitly the RQ factorization of the GQRD. The maximum likelihood estimation of the model when the covariance matrix is unknown is also considered.

Metadata

Item Type:	Article
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	11 May 2021 21:53
Last Modified:	09 Aug 2023 12:50
URI:	https://eprints.bbk.ac.uk/id/eprint/44239

Statistics

Archive Staff Only (login required)

Edit/View Item