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

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Author Info

  • Monica Billio

    ()
    (Department of Economics, University Of Venice Cà Foscari)

  • Roberto Casarin

    (Department of Economics, University Of Venice Cà Foscari)

  • Francesco Ravazzolo

    (Norges Bank)

  • Herman K. van Dijk

    (Erasmus University)

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.

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Bibliographic Info

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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Related research

Keywords: Density Forecast Combination; Survey Forecast; Bayesian Filtering; Sequential Monte Carlo.;

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References

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  1. repec:nsr:niesrd:320 is not listed on IDEAS
  2. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. van Dijk & Marno Verbeek, 2009. "Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time Varying Weights," Tinbergen Institute Discussion Papers 09-061/4, Tinbergen Institute.
  3. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers ubs0504, University of Brescia, Department of Economics.
  4. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
  5. Amit Goyal & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," Yale School of Management Working Papers amz2412, Yale School of Management, revised 01 Jan 2006.
  6. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
  7. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
  8. Caporin, Massimiliano & Preś, Juliusz, 2012. "Modelling and forecasting wind speed intensity for weather risk management," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3459-3476.
  9. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-time inflation forecasting in a changing world," Staff Reports 388, Federal Reserve Bank of New York.
  10. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
  11. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
  12. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
  13. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
  14. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, 02.
  15. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June.
  16. Monica Billio & Roberto Casarin, 2010. "Identifying business cycle turning points with sequential Monte Carlo methods: an online and real-time application to the Euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 145-167.
  17. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  18. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, 02.
  19. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
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Citations

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Cited by:
  1. Casarin, R. & Chang, C-L. & Jimenez-Martin, J-A. & McAleer, M.J. & Perez-Amaral, T., 2011. "Risk Management of Risk Under the Basel Accord: A Bayesian Approach to Forecasting Value-at-Risk of VIX Futures," Econometric Institute Research Papers EI2011-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Michal Franta & Jozef Barunik & Roman Horvath & Katerina Smidkova, 2011. "Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests," Working Papers 2011/10, Czech National Bank, Research Department.
  3. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
  4. Kocięcki, Andrzej & Kolasa, Marcin & Rubaszek, Michał, 2012. "A Bayesian method of combining judgmental and model-based density forecasts," Economic Modelling, Elsevier, vol. 29(4), pages 1349-1355.
  5. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Bayesian Combinations of Stock Price Predictions with an Application to the Amsterdam Exchange Index," Tinbergen Institute Discussion Papers 11-082/4, Tinbergen Institute.

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