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Parallelization Experience with Four Canonical Econometric Models using ParMitISEM

Bastürk, Nalan, Grassi, Stefano, Hoogerheide, Lennart, Van Dijk, Herman K. (2016) Parallelization Experience with Four Canonical Econometric Models using ParMitISEM. Econometrics, 4 (11). pp. 1-20. ISSN 2225-1146. E-ISSN 2225-1146. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:54594)

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This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm, introduced by Hoogerheide, Opschoor and Van Dijk (2012), 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.

Item Type: Article
Uncontrolled keywords: finite mixtures; Student-t distributions; Importance Sampling; MCMC; Metropolis-Hastings algorithm; Expectation Maximization; Bayesian inference
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
Divisions: Divisions > Division of Human and Social Sciences > School of Economics
Depositing User: Stefano Grassi
Date Deposited: 22 Mar 2016 11:57 UTC
Last Modified: 17 Aug 2022 12:20 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

Grassi, Stefano.

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