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Parallel Computation In Econometrics: A Simplified Approach

  • David Hendry
  • Neil Shephard
  • Jurgen Doornik

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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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2004-W16.

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Date of creation: 01 Jan 2003
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Handle: RePEc:oxf:wpaper:2004-w16
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  1. 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.
  2. 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.
  3. Manfred Gilli & Giorgio Pauletto, 1993. "Econometric Model Simulation on Parallel Computers," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 93.07, Institut d'Economie et Econométrie, Université de Genève.
  4. Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics 0303003, EconWPA.
  5. 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.
  6. 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.
  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. S. Illeris & G. Akehurst, 2001. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 21(1), pages 1-4, January.
  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, . "An omnibus test for univariate and multivariate normalit," Economics Papers W4&91., Economics Group, Nuffield College, University of Oxford.
  11. 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..
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