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Parallel Computation in Econometrics: A Simplified Approach

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Author Info

  • Jurgen A. Doornik

    ()
    (Nuffield College, University of Oxford)

  • Neil Shephard

    ()
    (Nuffield College, University of Oxford)

  • David F. Hendry

    ()
    (Nuffield College, University of Oxford)

Abstract

Parallel computation has a long history in econometric computing, but is not at all wide spread. We believe that a major impediment is the labour cost of coding for parallel architectures. Moreover, programs for specific hardware often become obsolete quite quickly. Our approach is to take a popular matrix programming language (Ox), and implement a message-passing interface using MPI. Next, object-oriented programming allows us to hide the specific parallelization code, so that a program does not need to be rewritten when it is ported from the desktop to a distributed network of computers. Our focus is on so-called embarrassingly parallel computations, and we address the issue of parallel random number generation.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/2004/w16/JADDFHNSHandbook.pdf
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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2004-W16.

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Length: 32 pages
Date of creation: 30 Jan 2004
Date of revision:
Handle: RePEc:nuf:econwp:0416

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

Related research

Keywords: Code optimization; Econometrics; High-performance computing; Matrix-programming language; Monte Carlo; MPI; Ox; Parallel computing; Random number generation.;

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References

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  1. Murphy, K. & Clint, M. & Perrott, R. H., 1999. "Re-engineering statistical software for efficient parallel execution," Computational Statistics & Data Analysis, Elsevier, vol. 31(4), pages 441-456, October.
  2. Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998. "Statistical algorithms for models in state space using SsfPack 2.2," Economics Series Working Papers 1998-W06, University of Oxford, Department of Economics.
  3. Nagurney, Anna & Takayama, Takashi & Zhang, Ding, 1995. "Massively parallel computation of spatial price equilibrium problems as dynamical systems," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 3-37.
  4. Nagurney, Anna & Zhang, Ding, 1998. "A massively parallel implementation of a discrete-time algorithm for the computation of dynamic elastic demand traffic problems modeled as projected dynamical systems," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1467-1485, August.
  5. David Hendry & Neil Shephard & Jurgen Doornik, 2001. "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Series Working Papers 2001-W22, University of Oxford, Department of Economics.
  6. Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics 0303003, EconWPA.
  7. Cribari-Neto, Francisco & Jensen, Mark J, 1997. "MATLAB as an Econometric Programming Environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(6), pages 735-44, Nov.-Dec..
  8. Chong, Yock Y & Hendry, David F, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Wiley Blackwell, vol. 53(4), pages 671-90, August.
  9. Manfred Gilli & Giorgio Pauletto, 1993. "Econometric Model Simulation on Parallel Computers," Research Papers by the Department of Economics, University of Geneva 93.07, Département des Sciences Économiques, Université de Genève.
  10. S. Illeris & G. Akehurst, 2001. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 21(1), pages 1-4, January.
  11. Jurgen A Doornik & Henrik Hansen, . "An omnibus test for univariate and multivariate normalit," Economics Papers W4&91., Economics Group, Nuffield College, University of Oxford.
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