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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, vol. 4(1), pages 1-20, March.
    • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:1:p:11-:d:65219
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    Citations

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    Cited by:

    1. Jamie L. Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Djik, 2024. "Flexible Negative Binomial Mixtures for Credible Mode Inference in Heterogeneous Count Data from Finance, Economics and Bioinformatics," Working Papers No 09/2024, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. 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.
    3. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman van Dijk, 2022. "A Flexible Predictive Density Combination Model for Large Financial Data Sets in Regular and Crisis Periods," Tinbergen Institute Discussion Papers 22-013/III, Tinbergen Institute.
    4. Stefano Grassi & Marco Lorusso & Francesco Ravazzolo, 2025. "Adaptive Importance Sampling Estimation of an Open Economy Model with Fiscal Policy," BEMPS - Bozen Economics & Management Paper Series BEMPS111, Faculty of Economics and Management at the Free University of Bozen.
    5. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023. "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Stefano Grassi & Marco Lorusso & Francesco Ravazzolo, 2021. "Adaptive Importance Sampling for DSGE Models," BEMPS - Bozen Economics & Management Paper Series BEMPS84, Faculty of Economics and Management at the Free University of Bozen.
    7. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.
    8. 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.

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    More about this item

    Keywords

    Importance sampling; parallel computing; MitISEM; MCMC;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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