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

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  • 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

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

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  1. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
  2. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005, Society for Computational Economics 438, Society for Computational Economics.
  3. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, Elsevier, vol. 138(1), pages 291-311, May.
  4. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, Elsevier, vol. 18(3), pages 421-438.
  5. Gambetti, Luca & D’Agostino, Antonello & Giannone, Domenico, 2010. "Macroeconomic forecasting and structural change," Working Paper Series, European Central Bank 1167, European Central Bank.
  6. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper, Federal Reserve Bank of Cleveland 1218, Federal Reserve Bank of Cleveland.
  7. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  8. 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.
  9. 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.
  10. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, 03.
  11. Roberto Casarin & Jean-Michel Marin, 2007. "Online data processing: comparison of Bayesian regularized particle filters," Working Papers, University of Brescia, Department of Economics 0703, University of Brescia, Department of Economics.
  12. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 23(1), pages 1-13.
  13. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers, Tinbergen Institute 12-118/III, Tinbergen Institute.
  14. Anne Sofie Jore & James Mitchell & Shaun Vahey, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," Reserve Bank of New Zealand Discussion Paper Series DP2008/18, Reserve Bank of New Zealand.
  15. Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 19(2), pages 145-78, April.
  16. 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.
  17. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
  18. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
  19. 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.
  20. James P. LeSage, 1998. "ECONOMETRICS: MATLAB toolbox of econometrics functions," Statistical Software Components T961401, Boston College Department of Economics.
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