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|
|JEL||Bayesian Analysis (jel C11), Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Forecasting and Other Model Applications (jel C53), Forecasting and Simulation (jel E37)|
|Series||Tinbergen Institute Discussion Paper Series|
|Journal||Discussion paper / Tinbergen Institute|
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