文章基本信息

标题：Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM
本地全文：下载
作者：BaÅŸtÃ¼rk, Nalan ; Grassi, Stefano ; Hoogerheide, Lennart 等
期刊名称：Econometrics
印刷版ISSN：2225-1146
出版年度：2016
卷号：4
期号：1
出版社：MDPI, Open Access Journal
摘要：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.
关键词：Importance sampling; parallel computing; MitISEM; MCMC