Parallelization Experience with Four Canonical Econometric Models using ParMitISEM

Nalan Baştürk, Stefano Grassi (), Lennart Hoogerheide and Herman van Dijk
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Lennart Hoogerheide: VU University Amsterdam, the Netherlands

No 16-005/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: 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.

Keywords: finite mixtures; Student-t distributions; Importance Sampling; MCMC; Metropolis-Hastings algorithm; Expectation Maximization; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C13 C23 C32 (search for similar items in EconPapers)
Date: 2016-01-22
New Economics Papers: this item is included in nep-cmp and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Journal Article: Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM (2016)
Working Paper: Parallelization experience with four canonical econometric models using ParMitISEM (2016)
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