Skip to main content
SpringerLink
Log in

Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI

  • Published:
Computational Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Adeli, H. (2000). High performance computing for large-scale analysis, optimization, and control. Journal of Aerospace Engineering, 13, 1–10.

    Google Scholar 

  • Amdahl, G.M. (1967). Validity of the single processor approach to achieving large scale computing capabilities. AFIPS Conference Proceedings, 483–485.

  • Ansorge, R.E., Carpenter, T.A., Hall L.D., Shaw N.R. and Williams G.B. (1999). Use of parallel supercomputing to design magnetic resonance systems. IEEE Transactions on Applied Superconductivity, 10, 1368–1371.

    Google Scholar 

  • Atkinson, A.C. (1994). Fast very robust methods for the detection of multiple outliers. Journal of the American Statistical Association, 89(428), 1329–1339.

    Google Scholar 

  • Chabini, I., Drissi-Kaitouni O. and Florian M. (1994). Parallel implementations of primal and dual algorithms for matrix balancing. In D. Belsley (ed.), Computational Techniques for Econometrics and Economic Analysis. Kluwer Academic Publishers.

  • Coleman II, W.J. (1993). Solving nonlinear dynamic models on parallel computers. Journal of Business & Economic Statistics, 11(3), 325–330.

    Google Scholar 

  • Foster, I. (1995). Designing and Building Parallel Programs. Addison-Wesley.

  • Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, R. and Sunderam, V. (1994). Parallel Virtual Machine: A Users Guide and Tutorial for Networked Parallel Computing. The MIT Press.

  • Gropp, W., Lusk, E. and Skjellum, A. (1999). Using MPI: Portable Parallel Programming with the Message-Passing Interface, 2nd edn. The MIT Press.

  • Gropp W., Lusk, E. and Thakur, R. (1999). Using MPI-2: Advanced Features of the Message Passing Interface, The MIT Press, 1999.

  • LAM Team, LAM Version 6.3.2. University of Notre Dame, http://www.mpi.nd.edu/lam/6.3.

  • Liu, C. and Rubin, D.B. (1996). Markov-normal analysis of iterative simulations before their convergence. Journal of Econometrics, 75, 69–78.

    Google Scholar 

  • Mineter, M.J. and Dowers, S. (1999). Parallel processing for geographical applications: a layered approach. Journal of Geographical Systems, 1, 61–74.

    Google Scholar 

  • Moffitt, R. (1992). Incentive effects of the U.S. welfare system: a review. Journal of Economic Literature, 30, 1–61.

    Google Scholar 

  • Nagurney, A. (1996). Parallel computation. In H.M. Amman, D.A. Kendrick, J. Rust (eds.), Handbook of Computational Economics, Volume 1. Elsevier.

  • Nagurney, A. and Eydeland, A. (1992). A splitting equilibration algorithm for the computation of large-scale constrained matrix problems: theoretical analysis and applications. In H. Amman, D. Belsley and L. Pau (eds.), Computational Economics and Econometrics. Kluwer Academic Press.

  • Snir, M., Otto, S., Huss-Lederman, S., Walker, D., and Dongarra, J. (1996). MPI: The Complete Reference. The MIT Press.

  • Sterling, T.L., Salmon, J., Becker, D.J., and Savarese, D.F. (1999). How to Build a Beowulf: A Guide to the Implementation and Application of PC Clusters. The MIT Press.

  • Swann, C.A. (1996). A dynamic analysis of marriage, labor force participation, and participation in the AFDC program. PhD Dissertation, University of Virginia.

Download references

Author information

Authors and Affiliations

  1. Department of Economics, SUNY-Stony Brook, Stony Brook, NY, 11794, U.S.A.

    Christopher A. Swann

Authors
  1. Christopher A. Swann
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Swann, C.A. Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI. Computational Economics 19, 145–178 (2002). https://doi.org/10.1023/A:1015021911216

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015021911216

Search

Navigation