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Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM

SSRN Electronic Journal
2016
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Metrics Details

  • Usage
    778
    • Abstract Views
      734
    • Downloads
      44

Article Description

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.

Bibliographic Details

Nalan Basturk; Stefano Grassi; Lennart F. Hoogerheide; H. K. van Dijk

Elsevier BV

finite mixtures; Student-t distributions; Importance Sampling; MCMC; Metropolis-Hastings algorithm; Expectation Maximization; Bayesian inference

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