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Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox

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Author Info

  • Roberto Casarin

    ()
    (University Ca’ Foscari of Venice and GRETA)

  • Stefano Grassi

    ()
    (Aarhus University and CREATES)

  • Francesco Ravazzolo

    ()
    (Norges Bank and BI Norwegian Business School)

  • Herman K. van Dijk

    ()
    (Erasmus University Rotterdam, VU University Amsterdam and Tinbergen Institute)

Abstract

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing and for Graphical Process Unit (GPU) parallel computing. For the GPU implementation we use the Matlab parallel computing toolbox and show how to use General Purposes GPU computing almost effortless. This GPU implementation comes with a speed up of the execution time up to seventy times compared to a standard CPU Matlab implementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version, through some simulation experiments and empirical applications.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2013-09.

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Length: 28
Date of creation: 04 Aug 2013
Date of revision:
Handle: RePEc:aah:create:2013-09

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Density Forecast Combination; Sequential Monte Carlo; Parallel Computing; GPU; Matlab;

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  1. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.
  2. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
  3. Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011. "Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 35(3), pages 386-393, March.
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  6. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, Elsevier, vol. 177(2), pages 213-232.
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