User-Friendly Parallel Computations with Econometric Examples

User-Friendly Parallel Computations with Econometric Examples This paper shows how a high-level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Economics Springer Journals

User-Friendly Parallel Computations with Econometric Examples

Computational Economics, Volume 26 (2) – Jan 1, 2005

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Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer Science + Business Media, Inc.
Subject
Economics; Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operation Research/Decision Theory; Behavioral/Experimental Economics; Math Applications in Computer Science
ISSN
0927-7099
eISSN
1572-9974
D.O.I.
10.1007/s10614-005-6868-2
Publisher site
See Article on Publisher Site

Abstract

This paper shows how a high-level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

Journal

Computational EconomicsSpringer Journals

Published: Jan 1, 2005

References

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