Seemingly unrelated regression model with unequal size observations: computational aspects
Journal
Computational Statistics and Data Analysis
Date Issued
November 2002
Author(s)
DOI
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.