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

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References

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  1. Amit Goval & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," NBER Working Papers 10483, National Bureau of Economic Research, Inc.
  2. 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., John Wiley & Sons, Ltd., vol. 29(1-2), pages 145-167.
  3. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier, Elsevier.
  4. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, Elsevier, vol. 138(1), pages 291-311, May.
  5. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
  6. Andrew Ang & Geert Bekaert & Min Wei, 2006. "Do macro variables, asset markets, or surveys forecast inflation better?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2006-15, Board of Governors of the Federal Reserve System (U.S.).
  7. repec:nsr:niesrd:320 is not listed on IDEAS
  8. Caporin, Massimiliano & Preś, Juliusz, 2012. "Modelling and forecasting wind speed intensity for weather risk management," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(11), pages 3459-3476.
  9. 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 weight," Working Paper, Norges Bank 2009/10, Norges Bank.
  10. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 23(1), pages 1-13.
  11. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2013. "Real-Time Inflation Forecasting in a Changing World," Journal of Business & Economic Statistics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 31(1), pages 29-44, January.
  12. 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, American Statistical Association, vol. 105(489), pages 25-35.
  13. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers, University of Brescia, Department of Economics ubs0504, University of Brescia, Department of Economics.
  14. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, Elsevier, vol. 150(2), pages 297-311, June.
  15. Geweke, John & Amisano, Gianni, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series, European Central Bank 0969, European Central Bank.
  16. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper, Norges Bank 2008/01, Norges Bank.
  17. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, American Finance Association, vol. 56(1), pages 329-352, 02.
  18. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, American Finance Association, vol. 55(1), pages 225-264, 02.
  19. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
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Citations

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Cited by:
  1. 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, Tinbergen Institute 11-082/4, Tinbergen 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, Czech National Bank, Research Department 2011/10, Czech National Bank, Research Department.
  3. Casarin, Roberto & Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & McAleer, Michael & Pérez-Amaral, Teodosio, 2013. "Risk management of risk under the Basel Accord: A Bayesian approach to forecasting Value-at-Risk of VIX futures," Mathematics and Computers in Simulation (MATCOM), Elsevier, Elsevier, vol. 94(C), pages 183-204.
  4. Kocięcki, Andrzej & Kolasa, Marcin & Rubaszek, Michał, 2012. "A Bayesian method of combining judgmental and model-based density forecasts," Economic Modelling, Elsevier, Elsevier, vol. 29(4), pages 1349-1355.
  5. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, Elsevier, vol. 177(2), pages 357-373.

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