Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/209974 
Year of Publication: 
2010
Series/Report no.: 
Working Paper No. 2010/29
Publisher: 
Norges Bank, Oslo
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 forevaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
Subjects: 
Bayesian filtering
sequential Monte Carlo
density forecast combination
survey forecast
JEL: 
C11
C15
C53
E37
Persistent Identifier of the first edition: 
ISBN: 
978-82-7553-586-1
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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