Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance

Abstract

A Bayesian semi-parametric dynamic model combination is proposed in order to deal with a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing combination weight dependence between models as well as over time. It introduces an information reduction step by using a clustering mechanism that allocates the large set of predictive densities into a smaller number of mutually exclusive subsets. The complexity of the approach is further reduced by making use of the class-preserving property of the logistic-normal distribution that is specified in the compositional dynamic factor model for the weight dynamics with latent factors defined on a reduced dimension simplex. The whole model is represented as a nonlinear state space model that allows groups of predictive models with corresponding combination weights to be updated with parallel clustering and sequential Monte Carlo filters. The approach 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

JEL Classification: C11, C15, C53, E37