Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86763
Authors: 
Billio, Monica
Casarin, Roberto
Ravazzolo, Francesco
van Dijk, Herman K.
Year of Publication: 
2011
Series/Report no.: 
Tinbergen Institute Discussion Paper 11-003/4
Abstract: 
Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
Subjects: 
Density Forecast Combination
Survey Forecast
Bayesian Filtering
Sequential Monte Carlo
JEL: 
C11
C15
C53
E37
Document Type: 
Working Paper

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