41 Pages Posted: 9 Jan 2011
Date Written: December 20, 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 for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo
JEL Classification: C11, C15, C53, E37
Suggested Citation: Suggested Citation
Billio, Monica and Casarin, Roberto and Ravazzolo, Francesco and van Dijk, H. K., Combining Predictive Densities Using Bayesian Filtering with Applications to US Economics Data (December 20, 2010). Norges Bank Working Paper No. 2010/29 . Available at SSRN: https://ssrn.com/abstract=1735421 or http://dx.doi.org/10.2139/ssrn.1735421
By Peter Mcadam