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Computationally-intensive Econometrics using a Distributed Matrix-programming Language

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Abstract

This paper reviews the need for powerful facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy to use generic software which is able to exploit the availability of cheap clusters of distributed computers. Our response is to extend, in a number of directions, the well known matrix-programming interpreted language Ox developed by the first author. We note three possible levels of extensions: (i) Ox with parallelization explicit in the Ox code; (ii) Ox with a parallelized run-time library; (iii) Ox with a parallelized interpreter. This paper studies and implements the first case, emphasizing the need for deterministic computing in science. We give examples in the context of financial economics and time-series modelling.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/2001/w22/hpc20013.pdf
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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2001-W22.

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Length: 23 pages
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Handle: RePEc:nuf:econwp:0122

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Web page: http://www.nuff.ox.ac.uk/economics/

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Keywords: Distributed computing; Econometrics; High-performance computing; Matrix-programming language;

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Citations

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Cited by:
  1. Jurgen A. Doornik & Neil Shephard & David F. Hendry, 2004. "Parallel Computation in Econometrics: A Simplified Approach," Economics Papers, Economics Group, Nuffield College, University of Oxford 2004-W16, Economics Group, Nuffield College, University of Oxford.
  2. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," UFAE and IAE Working Papers, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) 637.05, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  3. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
  4. Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 32(4), pages 353-382, November.
  5. Christopher Ferrall, 2005. "Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 25(4), pages 343-379, June.
  6. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.
  7. Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics, EconWPA 0303003, EconWPA.

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