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

  • Monica Billio

    (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice)

  • Roberto Casarin

    (University of Breccia and GRETA Assoc)

  • Francesco Ravazzolo

    (Norges Bank (Central Bank of Norway))

  • Herman K. van Dijk

    ()

    (Econometrics and Tinbergen Institutes, Erasmus University Rotterdam)

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.

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File URL: http://www.norges-bank.no/en/Published/Papers/Working-Papers/2010/WP-201029/
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Paper provided by Norges Bank in its series Working Paper with number 2010/29.

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Length: 39 pages
Date of creation: 21 Dec 2010
Date of revision:
Handle: RePEc:bno:worpap:2010_29
Note: First version:
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