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Solving Finite Mixture Models in Parallel

Author

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  • Christopher Ferrall

    (Queen's University)

Abstract

Many economic models are completed by finding a parameter vector that optimizes a function f, a task that only be accomplished by iterating from a starting vector. Use of a generic iterative optimizer to carry out this task can waste enormous amounts of computation when applied to a class of problems defined here as finite mixture models. The finite mixture class is large and important in economics and eliminating wasted computations requires only limited changes to standard code. Further, the approach described here greatly increases gains from parallel execution and opens possibilities for re-writing objective functions to make further efficiency gains.

Suggested Citation

  • Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics 0303003, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpco:0303003
    Note: Type of Document - PDF; prepared on MikTeX; pages: 47; figures: included/
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/comp/papers/0303/0303003.pdf
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    References listed on IDEAS

    as
    1. Peter Arcidiacono & John Bailey Jones, 2003. "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm," Econometrica, Econometric Society, vol. 71(3), pages 933-946, May.
    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. repec:wly:riskan:v:20:y:2000:i:3:p:295-296 is not listed on IDEAS
    4. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
    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. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    7. Jose-Victor Rios-Rull, 1997. "Computation of equilibria in heterogeneous agent models," Staff Report 231, Federal Reserve Bank of Minneapolis.
    8. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ferrall, Christopher, 2002. "Estimation and Inference in Social Experiments," Queen's Economics Department Working Papers 273435, Queen's University - Department of Economics.
    2. 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.
    3. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
    4. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    5. 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.

    More about this item

    Keywords

    Numerical Optimization; Heterogeneous Agent Models;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models

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