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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.

Suggested Citation

  • Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," UFAE and IAE Working Papers 637.05, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  • Handle: RePEc:aub:autbar:637.05
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    File URL: http://pareto.uab.es/wp/2005/63705.pdf
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    References listed on IDEAS

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    1. Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Springer;Society for Computational Economics, vol. 19(2), pages 145-178, April.
    2. 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.
    3. 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).
    4. 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 203-223, May-June.
    5. 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.
    6. repec:cup:etheor:v:12:y:1996:i:4:p:657-81 is not listed on IDEAS
    7. Michael Creel, 2005. "ParallelKnoppix," Grecs Computer Code 003.05, Research Group in Computation and Simulations (GRECS).
    8. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
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    Citations

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    Cited by:

    1. 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.
    2. Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 353-382, November.
    3. Michael Creel & Dennis Kristensen, 2012. "Estimation of dynamic latent variable models using simulated non‐parametric moments," Econometrics Journal, Royal Economic Society, vol. 15(3), pages 490-515, October.
    4. 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.
    5. Simon Peters & Ken Clark & Pascal Ekin & Anja Le Blanc & Stephen Pickles, 2007. "Grid Enabling Empirical Economics: A Microdata Application," Computational Economics, Springer;Society for Computational Economics, vol. 30(4), pages 349-370, November.
    6. Donghoon Lee & Matthew Wiswall, 2007. "A Parallel Implementation of the Simplex Function Minimization Routine," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 171-187, September.
    7. Michael Creel, 2008. "Using Parallelization to Solve a Macroeconomic Model: A Parallel Parameterized Expectations Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 343-352, November.

    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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