The computational efficiency of various algorithms for solving seemingly unrelated regressions (SUR) models is investigated. Some of the algorithms adapt known methods; others are new. The first transforms the SUR model to an ordinary linear model and uses the QR decomposition to solve it. Three others employ the generalized QR decomposition to solve the SUR model formulated as a generalized linear least-squares problem. Strategies to exploit the structure of the matrices involved are developed. The algorithms are reconsidered for solving the SUR model after it has been transformed to one of smaller dimensions. © 2003 Elsevier B.V. All rights reserved.
Foschi, P., Belsley, D.A., Kontoghiorghes, E.J. (2003). A comparative study of algorithms for solving seemingly unrelated regressions models. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 44(1-2), 3-35 [10.1016/s0167-9473(03)00028-8].
A comparative study of algorithms for solving seemingly unrelated regressions models
Foschi, PaoloPrimo
;
2003
Abstract
The computational efficiency of various algorithms for solving seemingly unrelated regressions (SUR) models is investigated. Some of the algorithms adapt known methods; others are new. The first transforms the SUR model to an ordinary linear model and uses the QR decomposition to solve it. Three others employ the generalized QR decomposition to solve the SUR model formulated as a generalized linear least-squares problem. Strategies to exploit the structure of the matrices involved are developed. The algorithms are reconsidered for solving the SUR model after it has been transformed to one of smaller dimensions. © 2003 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
