A comparative study of algorithms for solving seemingly unrelated regressions models

被引：18
|
作者：
Foschi, P
Belsley, DA
Kontoghiorghes, EJ
机构：
[1] Univ Neuchatel, Inst Informat, CH-2007 Neuchatel, Switzerland
[2] Boston Coll, Dept Econ, Chestnut Hill, MA 02467 USA
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2003年 / 44卷 / 1-2期
关键词：
SUR models; least-squares; QR decomposition;
D O I：
10.1016/S0167-9473(03)00028-8
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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. (C) 2003 Elsevier B.V. All rights reserved.
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页码：3 / 35
页数：33
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