Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/210079 
Year of Publication: 
2015
Series/Report no.: 
Working Paper No. 12/2015
Publisher: 
Norges Bank, Oslo
Abstract: 
A Bayesian nonparametric predictive model is introduced to construct time-varying weighted combinations of a large set of predictive densities. A clustering mechanism allocates these densities into a smaller number of mutually exclusive subsets. Using properties of the Aitchinson's geometry of the simplex, combination weights are defined with a probabilistic interpretation. The classpreserving property of the logistic-normal distribution is used to define a compositional dynamic factor model for the weight dynamics with latent factors defined on a reduced dimension simplex. Groups of predictive models with combination weights are updated with parallel clustering and sequential Monte Carlo filters. The procedure is applied to predict Standard & Poor's 500 index using more than 7000 predictive densities based on US individual stocks and finds substantial forecast and economic gains. Similar forecast gains are obtained in point and density forecasting of US real GDP, Inflation, Treasury Bill yield and employment using a large data set.
Subjects: 
GPU computing
Bayesian inference
density combination
large set of predictive densities
compositional factor models
nonlinear state space
JEL: 
C11
C15
C53
E37
Persistent Identifier of the first edition: 
ISBN: 
978-82-7553-875-6
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Working Paper
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