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Parallel Computation In Econometrics: A Simplified Approach

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  • David Hendry
  • Neil Shephard
  • Jurgen Doornik

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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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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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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  1. 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.
  2. 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.
  3. 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.
  4. Jurgen A. Doornik & David F. Hendry & Neil Shephard, . "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Papers 2001-W22, Economics Group, Nuffield College, University of Oxford.
  5. Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics 0303003, EconWPA.
  6. 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.
  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. 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.
  9. 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..
  10. 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.
  11. S. Illeris & G. Akehurst, 2001. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 21(1), pages 1-4, January.
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