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.

Additional Metadata
Keywords Baysian filtering, density forecast combination, sequential Monte Carlo, survey forecast
Publisher Tinbergen Institute
Persistent URL hdl.handle.net/1765/22330
Citation
Billio, M., Casarin, R., Ravazzolo, F., & van Dijk, H.K.. (2011). Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data (No. TI 2011-003/4). Discussion paper / Tinbergen Institute. Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/22330