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User-Friendly Parallel Computations with Econometric Examples

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    Abstract

    This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

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    File URL: http://pareto.uab.es/wp/2005/63705.pdf
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    Bibliographic Info

    Paper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 637.05.

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    Length: 25
    Date of creation: 10 Jan 2005
    Date of revision:
    Handle: RePEc:aub:autbar:637.05

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    Related research

    Keywords: parallel computing; Monte Carlo; bootstrapping; maximum likelihood; GMM; kernel regression;

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    1. Nagurney, Anna, 1996. "Parallel computation," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 7, pages 335-404 Elsevier.
    2. Michael Creel, 2005. "ParallelKnoppix," Grecs Computer Code 003.05, Research Group in Computation and Simulations (GRECS).
    3. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
    4. 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.
    5. repec:cup:etheor:v:12:y:1996:i:4:p:657-81 is not listed on IDEAS
    6. Michael D. Creel & Montserrat Farell, 2001. "Likelihood-Based Approaches to Modeling Demand for Medical Care," UFAE and IAE Working Papers 498.01, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    7. Cameron, A Colin & Johansson, Per, 1997. "Count Data Regression Using Series Expansions: With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 203-23, May-June.
    8. Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Society for Computational Economics, vol. 19(2), pages 145-78, April.
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    Cited by:
    1. 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.
    2. Simon Peters & Ken Clark & Pascal Ekin & Anja Le Blanc & Stephen Pickles, 2007. "Grid Enabling Empirical Economics: A Microdata Application," Computational Economics, Society for Computational Economics, vol. 30(4), pages 349-370, November.
    3. Michael Creel & Dennis Kristensen, 2009. "Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments," UFAE and IAE Working Papers 792.09, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    4. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
    5. Michael Creel, 2009. "A Data Mining Approach to Indirect Inference," UFAE and IAE Working Papers 788.09, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 25 Oct 2009.
    6. Michael Creel, 2008. "Using Parallelization to Solve a Macroeconomic Model: A Parallel Parameterized Expectations Algorithm," Computational Economics, Society for Computational Economics, vol. 32(4), pages 343-352, November.
    7. Donghoon Lee & Matthew Wiswall, 2007. "A Parallel Implementation of the Simplex Function Minimization Routine," Computational Economics, Society for Computational Economics, vol. 30(2), pages 171-187, September.

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