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Computationally-intensive Econometrics using a Distributed Matrix-programming Language

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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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
Date of creation:
Date of revision:
Handle: RePEc:nuf:econwp:0122
Contact details of provider: Web page: http://www.nuff.ox.ac.uk/economics/

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

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Cited by:

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

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