Billio, Monica Casarin, Roberto Ravazzolo, Francesco van Dijk, Herman K.
Year of Publication:
Tinbergen Institute Discussion Paper 11-003/4
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 for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
Density Forecast Combination Survey Forecast Bayesian Filtering Sequential Monte Carlo