Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171864
Authors: 
Baştürk, Nalan
Grassi, Stefano
Hoogerheide, Lennart
van Dijk, Herman K.
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 1 [Pages:] 1-20
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.
Subjects: 
importance sampling
parallel computing
MitISEM
MCMC
JEL: 
C11
C13
C32
C36
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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