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


  • Eroglu, Cuneyt
  • Croxton, Keely L.


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.

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  • 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

    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
    2. Klassen, Robert D. & Flores, Benito E., 2001. "Forecasting practices of Canadian firms: Survey results and comparisons," International Journal of Production Economics, Elsevier, vol. 70(2), pages 163-174, March.
    3. Dilek Onkal & M. Sinan Gonul, 2005. "Judgmental Adjustment: A Challenge for Providers and Users of Forecasts," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 13-17, June.
    4. Robert Carbone & Allan Andersen & Yvan Corriveau & Paul Piat Corson, 1983. "Comparing for Different Time Series Methods the Value of Technical Expertise Individualized Analysis, and Judgmental Adjustment," Management Science, INFORMS, vol. 29(5), pages 559-566, May.
    5. Lawrence, Michael & Makridakis, Spyros, 1989. "Factors affecting judgmental forecasts and confidence intervals," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(2), pages 172-187, April.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    7. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    8. Erez, Miriam & Gopher, Daniel & Arzi, Nira, 1990. "Effects of goal difficulty, self-set goals, and monetary rewards on dual task performance," Organizational Behavior and Human Decision Processes, Elsevier, vol. 47(2), pages 247-269, December.
    9. Robin M. Hogarth & Spyros Makridakis, 1981. "Forecasting and Planning: An Evaluation," Management Science, INFORMS, vol. 27(2), pages 115-138, February.
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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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