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Parallel Computation in Econometrics: A Simplified Approach

Author

Listed:
  • 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.

Suggested Citation

  • 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.
    • Handle: RePEc:nuf:econwp:0416
    as

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    File URL: http://www.nuff.ox.ac.uk/economics/papers/2004/w16/JADDFHNSHandbook.pdf
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    References listed on IDEAS

    as
    1. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    2. 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 735-744, Nov.-Dec..
    3. Anna Nagurney & Ding Zhang, "undated". "Massively Parallel Computation of Dynamic Traffic Problems Modeled as Projected Dynamical Systems," Computing in Economics and Finance 1996 _039, Society for Computational Economics.
    4. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    5. Jurgen A. Doornik & David F. Hendry & Neil Shephard, "undated". "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Papers 2001-W22, Economics Group, Nuffield College, University of Oxford.
    6. 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.
    7. 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.
    8. A. Abdelkhalek, A. Bilas and A. Michaelides, 2001. "Parallelization and Performance of Portfolio Choice Models," Computing in Economics and Finance 2001 114, Society for Computational Economics.
    9. 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.
    10. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    11. Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics 0303003, University Library of Munich, Germany.
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    More about this item

    Keywords

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