Dynamic predictive density combinations for large data sets in economics and finance
Roberto Casarin (),
Stefano Grassi (),
Francesco Ravazzolo () and
Herman van Dijk
No 2015/12, Working Paper from Norges Bank
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.
Keywords: Density Combination; Large Set of Predictive Densities; Compositional Factor Models; Nonlinear State Space; Bayesian Inference; GPU Computing (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
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Working Paper: Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:bno:worpap:2015_12
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