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Computationally-intensive Econometrics using a Distributed Matrix-programming Language

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Author Info

  • David Hendry
  • Neil Shephard
  • Jurgen Doornik

Abstract

This paper reviews the need for powerful computing 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 University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2001-W22.

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Date of creation: 01 Jan 2001
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Handle: RePEc:oxf:wpaper:2001-w22

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Web page: http://www.economics.ox.ac.uk/
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Keywords: Distributed computing; Econometrics; High-performance computing; Matrix-programming language;

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References

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Citations

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Cited by:
  1. David Hendry & Neil Shephard & Jurgen Doornik, 2003. "Parallel Computation In Econometrics: A Simplified Approach," Economics Series Working Papers 2004-W16, University of Oxford, Department of Economics.
  2. 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.
  3. Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics, EconWPA 0303003, EconWPA.
  4. 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.
  5. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005, Society for Computational Economics 438, Society for Computational Economics.
  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. 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).

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