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

  • 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)

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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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
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
  2. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
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  5. Matt P. Dziubinski & Stefano Grassi, 2012. "Heterogeneous Computing in Economics: A Simplified Approach," CREATES Research Papers 2012-15, School of Economics and Management, University of Aarhus.
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  14. repec:dgr:uvatin:20120118 is not listed on IDEAS
  15. 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.
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  18. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
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