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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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  2. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
  3. Dr. James Mitchell, 2005. "Evaluating, comparing and combining density forecasts using the KLIC with an application to the Bank of England and NIESR ÔfanÕ charts of inflation," NIESR Discussion Papers 777, National Institute of Economic and Social Research.
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  26. James Mitchell & Stephen G. Hall, 2005. "Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 995-1033, December.
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