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Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI

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  • Swann, Christopher A

Abstract

The computational difficulty of econometric problems has increased dramatically in recent years as econometricians examine more complicated models and utilize more sophisticated estimation techniques. Many problems in econometrics are "embarrassingly parallel" and can take advantage of parallel computing to reduce the wall clock time it takes to solve a problem. In this paper I demonstrate a method that can be used to solve a maximum likelihood problem using the MPI message passing library. The econometric problem is a simple multinomial logit model that does not require parallel computing but illustrates many of the problems one would confront when estimating more complicated models. Copyright 2002 by Kluwer Academic Publishers

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:19:y:2002:i:2:p:145-78
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    Cited by:

    1. Bound, John & Stinebrickner, Todd & Waidmann, Timothy, 2010. "Health, economic resources and the work decisions of older men," Journal of Econometrics, Elsevier, vol. 156(1), pages 106-129, May.
    2. 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.
    3. 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.
    4. Michael S. Delgado & Christopher F. Parmeter, 2013. "Embarrassingly Easy Embarrassingly Parallel Processing in R: Implementation and Reproducibility," Working Papers 2013-06, University of Miami, Department of Economics.
    5. 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).
    6. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    7. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    8. John Bound & Todd Stinebrickner & Timothy Waidman, 2004. "Using a Structural Retirement Model to Simulate the Effect of Changes to the OASDI and Medicare Programs," Working Papers wp091, University of Michigan, Michigan Retirement Research Center.
    9. 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.
    10. Christopher Ferrall, 2005. "Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution," Computational Economics, Springer;Society for Computational Economics, vol. 25(4), pages 343-379, June.
    11. 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.
    12. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 26(2), pages 107-128, October.
    13. Michael Creel, 2016. "A Note on Julia and MPI, with Code Examples," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 535-546, October.
    14. Nakano, Junji, 2004. "Parallel computing techniques," Papers 2004,27, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    15. 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.
    16. Kyle Klein & Julian Neira, 2014. "Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation," Computational Economics, Springer;Society for Computational Economics, vol. 43(4), pages 447-461, April.
    17. Michael Creel, 2004. "ParallelKnoppix - Rapid Deployment of a Linux Cluster for MPI Parallel Processing Using Non-Dedicated Computers," UFAE and IAE Working Papers 625.04, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).

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