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

  • Monica Billio

    ()

    (Department of Economics, University Of Venice C� Foscari)

  • Roberto Casarin

    (Department of Economics, University Of Venice C� Foscari)

  • Francesco Ravazzolo

    (Norges Bank)

  • Herman K. van Dijk

    (Erasmus University)

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.

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Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2012_16.

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Length: 41
Date of creation: 2012
Date of revision:
Handle: RePEc:ven:wpaper:2012_16
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