Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2016-01-22
Number: 16-005/III
Author-Name: Nalan Basturk
Author-Workplace-Name: Maastricht University, the Netherlands
Author-Name: Stefano Grassi
Author-Workplace-Name: University of Kent, United Kingdom
Author-Name: Lennart Hoogerheide
Author-Workplace-Name: VU University Amsterdam, the Netherlands
Author-Name: Herman K. van Dijk
Author-Workplace-Name: VU University Amsterdam, Erasmus University Rotterdam, the Netherlands
Title: Parallelization Experience with Four Canonical Econometric Models using ParMitISEM
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.
Classification-JEL: C11, C13, C23, C32
Keywords: finite mixtures, Student-t distributions, Importance Sampling, MCMC, Metropolis-Hastings algorithm, Expectation Maximization, Bayesian inference
File-Url: https://papers.tinbergen.nl/16005.pdf
File-Format: application/pdf
File-Size: 1334822 bytes
Handle: RePEc:tin:wpaper:20160005