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Estimating Loss Functions of Experts

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Author Info

  • Philip Hans Franses

    (Erasmus University Rotterdam)

  • Rianne Legerstee

    (Erasmus University Rotterdam)

  • Richard Paap

    (Erasmus University Rotterdam)

Abstract

We propose a new and simple methodology to estimate the loss function associated with experts' forecasts. Under the assumption of conditional normality of the data and the forecast distribution, the asymmetry parameter of the lin-lin and linex loss function can easily be estimated using a linear regression. This regression also provides an estimate for potential systematic bias in the forecasts of the expert. The residuals of the regression are the input for a test for the validity of the normality assumption. We apply our approach to a large data set of SKU-level sales forecasts made by experts and we compare the outcomes with those for statistical model-based forecasts of the same sales data. We find substantial evidence for asymmetry in the loss functions of the experts, with underprediction penalized more than overprediction.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-177/4.

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Date of creation: 16 Dec 2011
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Handle: RePEc:dgr:uvatin:20110177

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Web page: http://www.tinbergen.nl

Related research

Keywords: model forecasts; expert forecasts; loss functions; asymmetry; econometric models;

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References

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  1. Christoffersen, Peter F. & Diebold, Francis X., 1997. "Optimal Prediction Under Asymmetric Loss," Econometric Theory, Cambridge University Press, vol. 13(06), pages 808-817, December.
  2. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
  3. repec:bla:restud:v:72:y:2005:i:4:p:1107-1125 is not listed on IDEAS
  4. Christoffersen, Peter F & Diebold, Francis X, 1996. "Further Results on Forecasting and Model Selection under Asymmetric Loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 561-71, Sept.-Oct.
  5. Sanders, Nada R., 2009. "Comments on "Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning"," International Journal of Forecasting, Elsevier, vol. 25(1), pages 24-26.
  6. Robert C. Blattberg & Stephen J. Hoch, 1990. "Database Models and Managerial Intuition: 50% Model + 50% Manager," Management Science, INFORMS, vol. 36(8), pages 887-899, August.
  7. M A Clatworthy & D Peel & P F Pope, 2006. "Are analysts’ loss functions asymmetric?," Working Papers 574591, Lancaster University Management School, Economics Department.
  8. Philip Hans Franses & Rianne Legerstee, 2010. "Do experts' adjustments on model-based SKU-level forecasts improve forecast quality?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 331-340.
  9. Önkal, Dilek, 2009. "Comments on "Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning"," International Journal of Forecasting, Elsevier, vol. 25(1), pages 30-31.
  10. Flores, Benito E., 2009. "Comments on "Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning"," International Journal of Forecasting, Elsevier, vol. 25(1), pages 27-29.
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Cited by:
  1. Nicolaas van der Wath, 2013. "Comparing the BER’s forecasts," Working Papers 23/2013, Stellenbosch University, Department of Economics.

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