Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data
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||Baysian filtering, density forecast combination, sequential Monte Carlo, survey forecast|
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