Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2013-04-09
Revision-Date: 2015-01-16
Number: 13-055/III
Author-Name: Roberto Casarin
Author-Workplace-Name: University Ca' Foscari of Venice and GRETA
Author-Name: Stefano Grassi
Author-Workplace-Name: CREATES, Aarhus University
Author-Name: Francesco Ravazzolo
Author-Workplace-Name: Norges Bank, and BI Norwegian Business School
Author-Name: Herman K. van Dijk
Author-Workplace-Name: Erasmus University Rotterdam, and VU University Amsterdam
Title: Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox
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.
Classification-JEL: C11, C15, C53, E37
Keywords: Density Forecast Combination, Sequential Monte Carlo, Parallel Computing, GPU, Matlab
File-Url: https://papers.tinbergen.nl/13055.pdf
File-Format: application/pdf
File-Size: 971406 bytes
Handle: RePEc:tin:wpaper:20130055