Combining predictive densities using Bayesian filtering with applications to US economic data

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

No 2012_16, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

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)
Pages: 41
Date: 2012
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data (2011)
Working Paper: Combining predictive densities using Bayesian filtering with applications to US economics data (2010)
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