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Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM

Author

Listed:
  • Nalan Baştürk

    () (Department of Quantitative Economics, School of Business and Economics, Maastricht University, Maastricht 6211LM, The Netherlands)

  • Stefano Grassi

    () (School of Economics, Keynes College, University of Kent, Canterbury CT27NP, UK)

  • Lennart Hoogerheide

    () (Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands)

  • Herman K. Van Dijk

    () (Department of Econometrics and Tinbergen Institute, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
    Econometric Institute and Tinbergen Institute, Erasmus School of Economics, Erasmus University, Rotterdam, 3062PA, The Netherlands)

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.

Suggested Citation

  • Nalan Baştürk & Stefano Grassi & Lennart Hoogerheide & Herman K. Van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-1, March.
    • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:1:p:11-:d:65219
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Baştürk, N. & Borowska, A. & Grassi, S. & Hoogerheide, L. & van Dijk, H.K., 2019. "Forecast density combinations of dynamic models and data driven portfolio strategies," Journal of Econometrics, Elsevier, vol. 210(1), pages 170-186.
    2. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.
    3. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.

    More about this item

    Keywords

    Importance sampling; parallel computing; MitISEM; MCMC;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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