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Forecaster Diversity and the Benefits of Combining Forecasts

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Author Info

  • Roy Batchelor

    (Department of Banking and Finance, City University Business School, Frobisher Crescent, Barbican, London EC2Y 8HB, UK)

  • Pami Dua

    (Department of Economics, University of Connecticut, Scofieldtown Road, Stamford, Connecticut 06903)

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Abstract

The expected error variance of a combined forecast is necessarily lower than that of an individual forecast, but in practice there may be considerable variation around these expected values. This paper introduces a measure of the benefit from combining, the probability of a reduction in error variance, which recognizes this problem. The measure is applied to data on the forecasts and forecasting methods of a panel of U.S. economists to determine how the benefits of combining vary with the number of forecasts combined, and with the diversity in theories and techniques among the component forecasts.

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File URL: http://dx.doi.org/10.1287/mnsc.41.1.68
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 41 (1995)
Issue (Month): 1 (January)
Pages: 68-75

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Handle: RePEc:inm:ormnsc:v:41:y:1995:i:1:p:68-75

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Related research

Keywords: forecasting; combined forecasts;

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Citations

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Cited by:
  1. Giordani, Paolo & Soderlind, Paul, 2000. "Inflation Forecast Uncertainty," Working Paper Series in Economics and Finance 384, Stockholm School of Economics, revised 09 Oct 2000.
  2. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
  3. Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study," Ifo Working Paper Series Ifo Working Paper No. 65, Ifo Institute for Economic Research at the University of Munich.
  4. repec:lan:wpaper:425 is not listed on IDEAS
  5. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
  6. H Stekler & R A Fildes, 1999. "The state of macroeconomic forecasting," Working Papers 539557, Lancaster University Management School, Economics Department.
  7. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
  8. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  9. repec:lan:wpaper:413 is not listed on IDEAS
  10. Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, vol. 26(3), pages 498-510, July.
  11. Krkoska, Libor & Teksoz, Utku, 2009. "How reliable are forecasts of GDP growth and inflation for countries with limited coverage?," Economic Systems, Elsevier, vol. 33(4), pages 376-388, December.
  12. Kumar, V. & Nagpal, Anish & Venkatesan, Rajkumar, 2002. "Forecasting category sales and market share for wireless telephone subscribers: a combined approach," International Journal of Forecasting, Elsevier, vol. 18(4), pages 583-603.
  13. Liginlal, Divakaran & Ow, Terence T., 2005. "On policy capturing with fuzzy measures," European Journal of Operational Research, Elsevier, vol. 167(2), pages 461-474, December.
  14. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
  15. Lahiri, Kajal & Liu, Fushang, 2005. "ARCH models for multi-period forecast uncertainty-a reality check using a panel of density forecasts," MPRA Paper 21693, University Library of Munich, Germany.
  16. repec:lan:wpaper:470 is not listed on IDEAS
  17. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
  18. Armstrong, J. Scott & Morwitz, Vicki G. & Kumar, V., 2000. "Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 383-397.

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