Norges Bank

Working Paper

Dynamic predictive density combinations for large data sets in economics and finance

Author:
Roberto Casarin, Stefano Grassi, Francesco Ravazzolo and Herman K. van Dijk
Series:
Working Paper
Number:
12/2015

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.

Norges Bank’s working papers present research projects and reports that are generally not in their final form. Other analyses by Norges Bank’s economists are also included in the series. The views and conclusions in these documents are those of the authors.

Norges Bank’s Working Papers are also distributed by RepEcSSRN and BIS Central Bank Research Hub.

ISSN 1502-8190 (online)

Published 5 August 2015 11:00
Published 5 August 2015 11:00