Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/210070 
Year of Publication: 
2015
Series/Report no.: 
Working Paper No. 03/2015
Publisher: 
Norges Bank, Oslo
Abstract: 
We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and Gneiting and Ranjan (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.
Subjects: 
Bayesian nonparametrics
forecast calibration
forecast combination
density forecast
beta mixtures
slice sampling
JEL: 
B06
C13
C14
C51
C53
Persistent Identifier of the first edition: 
ISBN: 
978-82-7553-847-3
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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