Solving Finite Mixture Models in Parallel
AbstractMany economic models are completed by finding a parameter vector that optimizes a function f, a task that only be accomplished by iterating from a starting vector. 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by EconWPA in its series Computational Economics with number 0303003.
Length: 47 pages
Date of creation: 31 Mar 2003
Date of revision:
Note: Type of Document - PDF; prepared on MikTeX; pages: 47; figures: included/
Contact details of provider:
Web page: http://220.127.116.11
Numerical Optimization; Heterogeneous Agent Models;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-04-09 (All new papers)
- NEP-CMP-2003-04-09 (Computational Economics)
- NEP-ECM-2003-04-12 (Econometrics)
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.:
- 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.
- 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.
- Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
- David Hendry & Neil Shephard & Jurgen Doornik, 2001.
"Computationally-intensive Econometrics using a Distributed Matrix-programming Language,"
Economics Series Working Papers
2001-W22, University of Oxford, Department of Economics.
- 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.
- Jose-Victor Rios-Rull, 1997. "Computation of equilibria in heterogeneous agent models," Staff Report 231, Federal Reserve Bank of Minneapolis.
- 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.
- 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.
- 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.
- Jurgen A. Doornik & Neil Shephard & David F. Hendry, 2004.
"Parallel Computation in Econometrics: A Simplified Approach,"
2004-W16, Economics Group, Nuffield College, University of Oxford.
- David Hendry & Neil Shephard & Jurgen Doornik, 2003. "Parallel Computation In Econometrics: A Simplified Approach," Economics Series Working Papers 2004-W16, University of Oxford, Department of Economics.
- 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.
- Christopher Ferrall, 2002. "Estimation and Inference in Social Experiments," General Economics and Teaching 0209001, EconWPA.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA).
If references are entirely missing, you can add them using this form.