Combining predictive densities using Bayesian filtering with applications to US economics data
Monica Billio,
Roberto Casarin,
Francesco Ravazzolo and
Herman van Dijk
No 2010/29, Working Paper from Norges Bank
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 forevaluating 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 (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 E37 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2010-12-21
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
Note: First version:
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
https://www.norges-bank.no/en/news-events/news-pub ... pers/2010/WP-201029/
Related works:
Working Paper: Combining predictive densities using Bayesian filtering with applications to US economic data (2012) 
Working Paper: Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data (2011) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bno:worpap:2010_29
Access Statistics for this paper
More papers in Working Paper from Norges Bank Contact information at EDIRC.
Bibliographic data for series maintained by ().