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

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  • Christopher Ferrall

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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  • 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.
    • Handle: RePEc:kap:compec:v:25:y:2005:i:4:p:343-379
    DOI: 10.1007/s10614-005-6413-3
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    5. Donghoon Lee, 2005. "An Estimable Dynamic General Equilibrium Model Of Work, Schooling, And Occupational Choice," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(1), pages 1-34, February.
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    Cited by:

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    2. 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.
    3. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    4. Alexander W. Cappelen & James Konow & Erik ?. S?rensen & Bertil Tungodden, 2013. "Just Luck: An Experimental Study of Risk-Taking and Fairness," American Economic Review, American Economic Association, vol. 103(4), pages 1398-1413, June.
    5. 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.
    6. Alexander W. Cappelen & Karl Ove Moene & Erik Ø. Sørensen & Bertil Tungodden, 2008. "Rich meets Poor - An International Fairness Experiment," Tinbergen Institute Discussion Papers 08-098/3, Tinbergen Institute.
    7. 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.
    8. 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.
    9. Christopher Ferrall, 2012. "Explaining and Forecasting Results of the Self-sufficiency Project," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(4), pages 1495-1526.
    10. Christopher Ferrall, 2020. "Object Oriented (Dynamic) Programming: Replication, Innovation and "Structural" Estimation," Working Paper 1432, Economics Department, Queen's University.
    11. José Dias Curto, 2024. "Recalculate Without Recomputing," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3279-3294, December.
    12. 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.

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