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Parallel Strategies for Solving SURE Models with Variance Inequalities and Positivity of Correlations Constraints

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  • Erricos Kontoghiorghes
  • Elias Dinenis

    (City University Business School)

  • Dennis Parkinson

    (University of London)

Abstract

The problem of computing estimates of parameters in SURE models with variance inequalities and positivity of correlations constraints is considered. Efficient algorithms that exploit the block bidiagonal structure of the data matrix are presented. The computational complexity of the main matrix factorizations is analyzed. A compact method to solve the model with proper subset regressors is proposed. Citation Copyright 2000 by Kluwer Academic Publishers.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1997 with number 45.

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Handle: RePEc:sce:scecf7:45

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Postal: CEF97, Stanford University, Department of Economics, Stanford CA USA
Web page: http://bucky.stanford.edu/cef97/
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Cited by:
  1. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
  2. Foschi, Paolo & Belsley, David A. & Kontoghiorghes, Erricos J., 2003. "A comparative study of algorithms for solving seemingly unrelated regressions models," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 3-35, October.
  3. Foschi, Paolo & Kontoghiorghes, Erricos J., 2003. "Estimating seemingly unrelated regression models with vector autoregressive disturbances," Journal of Economic Dynamics and Control, Elsevier, vol. 28(1), pages 27-44, October.
  4. Foschi, Paolo & Kontoghiorghes, Erricos J., 2002. "Seemingly unrelated regression model with unequal size observations: computational aspects," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 211-229, November.

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