Combining predictive densities using Bayesian filtering with applications to US economics data
- Monica Billio, Roberto Casarin, Francesco Ravazzolo and Herman K. van Dijk
- Working Paper
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.
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ISSN 1502-8190 (online)