Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87182
Authors: 
Casarin, Roberto
Grassi, Stefano
Ravazzolo, Francesco
van Dijk, Herman K.
Year of Publication: 
2013
Series/Report no.: 
Tinbergen Institute Discussion Paper 13-055/III
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.
Subjects: 
Density Forecast Combination
Sequential Monte Carlo
Parallel Computing
GPU
Matlab
JEL: 
C11
C15
C53
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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