Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2011-01-06
Number: 11-003/4
Author-Name: Monica Billio
Author-Email: billio@unive.it
Author-Workplace-Name: University Ca'Foscari di Venezia
Author-Name: Roberto Casarin
Author-Workplace-Name: University Ca'Foscari di Venezia
Author-Name: Francesco Ravazzolo
Author-Workplace-Name: Norges Bank
Author-Name: Herman K. van Dijk
Author-Workplace-Name: Erasmus University Rotterdam
Title: Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data
Abstract: 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.
Classification-JEL: C11, C15, C53, E37
Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo
File-Url: https://papers.tinbergen.nl/11003.pdf
File-Format: application/pdf
File-Size: 443009 bytes
Handle: RePEc:tin:wpaper:20110003