Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data

Monica Billio, Roberto Casarin, Francesco Ravazzolo and Herman van Dijk

No 11-003/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

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)
Date: 2011-01-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://papers.tinbergen.nl/11003.pdf (application/pdf)

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 (2010)
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:tin:wpaper:20110003

Access Statistics for this paper

More papers in Tinbergen Institute Discussion Papers from Tinbergen Institute Contact information at EDIRC.
Bibliographic data for series maintained by Tinbergen Office +31 (0)10-4088900 ().