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Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution

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  • Christopher Ferrall

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Abstract

Many economic models are completed by finding a parameter vector θ that optimizes a function f(θ), a task that can only be accomplished by iterating from a starting vector θ 0. 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. Copyright Springer Science + Business Media, Inc. 2005

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 25 (2005)
Issue (Month): 4 (June)
Pages: 343-379

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Handle: RePEc:kap:compec:v:25:y:2005:i:4:p:343-379

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: heterogeneous agent models; numerical optimization;

References

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  1. Susumu Imai & Neelam Jain, 2005. "Bayesian Estimation of Dynamic Discrete Choice Models," 2005 Meeting Papers 432, Society for Economic Dynamics.
  2. Arcidiacono, Peter & Jones, John B., 2000. "Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm," Working Papers 00-16, Duke University, Department of Economics.
  3. 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.
  4. 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.
  5. Jurgen A. Doornik & David F. Hendry & Neil Shephard, . "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Papers 2001-W22, Economics Group, Nuffield College, University of Oxford.
  6. Hotz, V Joseph & Miller, Robert A, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 497-529, July.
  7. Victor Aguirregabiria & Pedro Mira, 1999. "Swapping the Nested Fixed-Point Algorithm: a Class of Estimators for Discrete Markov Decision Models," Computing in Economics and Finance 1999 332, Society for Computational Economics.
  8. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, Spring.
  9. 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.
  10. Christopher A. Swann, 2001. "Software for parallel computing: the LAM implementation of MPI," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 185-194.
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Citations

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Cited by:
  1. 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.
  2. Pedro Mira & Victor Aguirregabiria, 2007. "Dynamic Discrete Choice Structural Models: A Survey," Working Papers wp2007_0711, CEMFI.
  3. Christopher Ferrall, 2012. "Explaining and Forecasting Results of the Self-sufficiency Project," Review of Economic Studies, Oxford University Press, vol. 79(4), pages 1495-1526.
  4. Cappelen, Alexander W & Konow, James & Sorensen, Erik O & Tungodden, Bertil, 2010. "Just luck: an experimental study of risk taking and fairness," MPRA Paper 24475, University Library of Munich, Germany.
  5. Kyle Klein & Julian Neira, 2014. "Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation," Computational Economics, Society for Computational Economics, vol. 43(4), pages 447-461, April.
  6. Alexander W. Cappelen & Astri Drange Hole & Erik Ø Sørensen & Bertil Tungodden, 2007. "The Pluralism of Fairness Ideals: An Experimental Approach," American Economic Review, American Economic Association, vol. 97(3), pages 818-827, June.
  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.
  8. Alexander W. Cappelen & Karl Ove Moene & Erik �. S�rensen & Bertil Tungodden, 2008. "Rich meets Poor - An International Fairness Experiment," Tinbergen Institute Discussion Papers 08-098/3, Tinbergen Institute.
  9. Cappelen, Alexander W. & Sørensen, Erik Ø. & Tungodden, Bertil, 2010. "Responsibility for what? Fairness and individual responsibility," European Economic Review, Elsevier, vol. 54(3), pages 429-441, April.

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