Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130490
Authors: 
Basturk, Nalan
Grassi, Stefano
Hoogerheide, Lennart
van Dijk, Herman K.
Year of Publication: 
2016
Series/Report no.: 
Tinbergen Institute Discussion Paper 16-005/III
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.
Subjects: 
finite mixtures
Student-t distributions
Importance Sampling
MCMC
Metropolis-Hastings algorithm
Expectation Maximization
Bayesian inference
JEL: 
C11
C13
C23
C32
Document Type: 
Working Paper

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