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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
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Related works:
Working Paper: Combining predictive densities using Bayesian filtering with applications to US economic data (2012) Downloads
Working Paper: Combining predictive densities using Bayesian filtering with applications to US economics data (2010) Downloads
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