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Online Forecast Combination for Dependent Heterogeneous Data

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Author Info

  • Sancetta, A.

Abstract

This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific conditions on the nature of time variation. Some experimental evidence to confirm the results is provided.

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File URL: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe0718.pdf
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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0718.

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Length: 29
Date of creation: Apr 2007
Date of revision:
Handle: RePEc:cam:camdae:0718

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Related research

Keywords: Forecast Combination; Model Selection; Multiplicative Update; Non-asymptotic Bound; On-line Learning.;

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References

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  1. Francis X. Diebold & Peter Pauly, 1987. "The use of prior information in forecast combination," Special Studies Papers 218, Board of Governors of the Federal Reserve System (U.S.).
  2. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
  3. Sancetta, Alessio, 2007. "Online forecast combinations of distributions: Worst case bounds," Journal of Econometrics, Elsevier, vol. 141(2), pages 621-651, December.
  4. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June.
  5. László Györfi & Gábor Lugosi & Frederic Udina, 2006. "Nonparametric Kernel-Based Sequential Investment Strategies," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 337-357.
  6. Samuelson, Paul A., 1979. "Why we should not make mean log of wealth big though years to act are long," Journal of Banking & Finance, Elsevier, vol. 3(4), pages 305-307, December.
  7. David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
  8. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  9. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  10. Elliott, Graham & Timmermann, Allan G, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
  11. Kohler, Michael & Krzyzak, Adam & Walk, Harro, 2006. "Rates of convergence for partitioning and nearest neighbor regression estimates with unbounded data," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 311-323, February.
  12. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  13. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
  14. Yang, Yuhong, 2004. "Combining Forecasting Procedures: Some Theoretical Results," Econometric Theory, Cambridge University Press, vol. 20(01), pages 176-222, February.
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Cited by:
  1. Assenmacher-Wesche, Katrin & Pesaran, M. Hashem, 2007. "Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows," IZA Discussion Papers 3071, Institute for the Study of Labor (IZA).

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