Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution
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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Volume (Year): 25 (2005)
Issue (Month): 4 (June)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Peter Arcidiacono & John Bailey Jones, 2003.
"Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm,"
Econometric Society, vol. 71(3), pages 933-946, 05.
- Arcidiacono, Peter & Jones, John B., 2000. "Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm," Working Papers 00-16, Duke University, Department of Economics.
- V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 497-529.
- 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.
- 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.
- Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
- Susumu Imai & Neelam Jain, 2005. "Bayesian Estimation of Dynamic Discrete Choice Models," 2005 Meeting Papers 432, Society for Economic Dynamics.
- Susumu Imai & Neelam Jain & Andrew Ching, 2006. "Bayesian Estimation of Dynamic Discrete Choice Models," Working Papers 1118, Queen's University, Department of Economics.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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)