Artikel

Parallelization experience with four canonical econometric models using ParMitISEM

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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.

Sprache
Englisch

Erschienen in
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 4 ; Year: 2016 ; Issue: 1 ; Pages: 1-20 ; Basel: MDPI

Klassifikation
Wirtschaft
Bayesian Analysis: General
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Multiple or Simultaneous Equation Models: Instrumental Variables (IV) Estimation
Thema
importance sampling
parallel computing
MitISEM
MCMC

Ereignis
Geistige Schöpfung
(wer)
Baştürk, Nalan
Grassi, Stefano
Hoogerheide, Lennart
van Dijk, Herman K.
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2016

DOI
doi:10.3390/econometrics4010011
Handle
Letzte Aktualisierung
31.01.2025, 20:24 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Original beim Datenpartner anzeigen

Objekttyp

  • Artikel

Beteiligte

  • Baştürk, Nalan
  • Grassi, Stefano
  • Hoogerheide, Lennart
  • van Dijk, Herman K.
  • MDPI

Entstanden

  • 2016

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