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Biases in judgmental adjustments of statistical forecasts: The role of individual differences

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  • Eroglu, Cuneyt
  • Croxton, Keely L.

Abstract

Judgmental adjustments of statistical forecasts are widely used for improving forecast accuracy. Despite the overall effectiveness of this method, it may allow forecasters to introduce biases in statistical forecasts when they judgmentally adjust them. This paper considers three types of bias: (1) optimism bias, (2) anchoring bias, and (3) overreaction bias. We explore the effects of particular individual differences, specifically personality, motivational orientation, and work locus of control, on forecasting biases. The results indicate that a forecaster's personality and motivational orientation have significant effects on forecasting biases, whereas work locus of control has no effect on forecasting biases. Our analysis further indicates that experience, work locus of control and motivational orientation drive a forecaster's willingness to judgmentally adjust a statistical forecast.

Suggested Citation

  • Eroglu, Cuneyt & Croxton, Keely L., 2010. "Biases in judgmental adjustments of statistical forecasts: The role of individual differences," International Journal of Forecasting, Elsevier, vol. 26(1), pages 116-133, January.
  • Handle: RePEc:eee:intfor:v:26:y::i:1:p:116-133
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:proeco:v:191:y:2017:i:c:p:85-96 is not listed on IDEAS
    2. Chang, Chia Lin & Franses, Philip Hans & Mcaleer, Michael, 2012. "Evaluating Individual and Mean Non-Replicable Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 22-43, September.
    3. Furnham, Adrian & Boo, Hua Chu, 2011. "A literature review of the anchoring effect," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(1), pages 35-42, February.
    4. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    5. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2014. "Evaluating Macroeconomic Forecasts: A Concise Review Of Some Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 195-208, April.
    6. Goodwin, Paul & Fildes, Robert & Lawrence, Michael & Stephens, Greg, 2011. "Restrictiveness and guidance in support systems," Omega, Elsevier, vol. 39(3), pages 242-253, June.
    7. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.
    8. Hammond, Robert G. & Morrill, Thayer, 2016. "Personality traits and bidding behavior in competing auctions," Journal of Economic Psychology, Elsevier, vol. 57(C), pages 39-55.
    9. repec:eee:jobhdp:v:144:y:2018:i:c:p:171-186 is not listed on IDEAS
    10. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    11. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.
    12. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.

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