Computationally efficient methods for estimating the updated-observations SUR models

Authors:
Petko I. Yanev;Erricos J. Kontoghiorghes
Affiliations:
IRISA/INRIA -- Rennes, Campus de Beaulieu, 35042 Rennes cedex, France and Faculty of Mathematics and Informatics, University of Plovdiv “Paisii Hilendarski”, Bulgaria;Department of Public and Business Administration, University of Cyprus, CY-1678 Nicosia, Cyprus and School of Computer Science and Information Systems, Birkbeck College, University of London, UK
Venue:
Applied Numerical Mathematics
Year:
2007

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Abstract

Computational strategies for estimating the seemingly unrelated regressions model after been updated with new observations are proposed. A sequential block algorithm based on orthogonal transformations and rich in BLAS-3 operations is proposed. It exploits efficiently the sparse structure of the data matrix and the Cholesky factor of the variance-covariance matrix. A parallel version of the new estimation algorithms for two important classes of models is considered. The parallel algorithm utilizes an efficient distribution of the matrices over the processors and has low inter-processor communication. Theoretical and experimental results are presented and analyzed. The parallel algorithm is found for these classes of models to be scalable and efficient.