Combining Predictive Densities Using Bayesian Filtering with Applications to Us Economics Data - Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk (Norges Bank, 2010)
Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures forevaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
hdl:11250/2497432
Keywords: C11; C15; C53; E37; 330; Bayesian filtering; sequential Monte Carlo; density forecast combination; survey forecast