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Econometrics 2016, 4(1), 11; doi:10.3390/econometrics4010011

Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM

1
Department of Quantitative Economics, School of Business and Economics, Maastricht University, Maastricht 6211LM, The Netherlands
2
School of Economics, Keynes College, University of Kent, Canterbury CT27NP, UK
3
Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
4
Econometric Institute and Tinbergen Institute, Erasmus School of Economics, Erasmus University, Rotterdam, 3062PA, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Francesco Ravazzolo and Roberto Casarin
Received: 15 September 2015 / Revised: 7 January 2016 / Accepted: 28 January 2016 / Published: 7 March 2016
(This article belongs to the Special Issue Computational Complexity in Bayesian Econometric Analysis)
View Full-Text   |   Download PDF [1902 KB, uploaded 7 March 2016]   |  

Abstract

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on model parameters and probabilities. We present and discuss four canonical econometric models using a Graphics Processing Unit and a multi-core Central Processing Unit version of the MitISEM algorithm. The results show that the parallelization of the MitISEM algorithm on Graphics Processing Units and multi-core Central Processing Units is straightforward and fast to program using MATLAB. Moreover the speed performance of the Graphics Processing Unit version is much higher than the Central Processing Unit one. View Full-Text
Keywords: Importance sampling; parallel computing; MitISEM; MCMC Importance sampling; parallel computing; MitISEM; MCMC
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Baştürk, N.; Grassi, S.; Hoogerheide, L.; van Dijk, H.K. Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM. Econometrics 2016, 4, 11.

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