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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

    (CREATES, Aarhus University)

  • Francesco Ravazzolo

    (Norges Bank, and BI Norwegian Business School)

  • Herman K. van Dijk

    (Erasmus University Rotterdam, and VU University Amsterdam)

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 Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 13-055/III.

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Date of creation: 09 Apr 2013
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Handle: RePEc:dgr:uvatin:20130055

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Web page: http://www.tinbergen.nl

Related research

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

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  1. Korobilis, Dimitris, 2009. "VAR forecasting using Bayesian variable selection," MPRA Paper 21124, University Library of Munich, Germany.
  2. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
  3. Roberto Casarin & Jean-Michel Marin, 2007. "Online data processing: comparison of Bayesian regularized particle filters," Working Papers 0703, University of Brescia, Department of Economics.
  4. 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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  8. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
  9. Eric M. Aldrich & Jesús Fernández-Villaverde & A. Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," NBER Working Papers 15909, National Bureau of Economic Research, Inc.
  10. 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., vol. 25(4), pages 621-634.
  11. 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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  13. 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.
  14. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  15. James P. LeSage, 1998. "ECONOMETRICS: MATLAB toolbox of econometrics functions," Statistical Software Components T961401, Boston College Department of Economics.
  16. D'Agostino, Antonello & Gambetti, Luca & Giannone, Domenico & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," Research Technical Papers 8/RT/09, Central Bank of Ireland.
  17. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
  18. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, vol. 177(2), pages 213-232.
  19. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
  20. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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