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Fast and frugal forecasting

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  • Goldstein, Daniel G.
  • Gigerenzer, Gerd

Abstract

Simple statistical forecasting rules, which are usually simplifications of classical models, have been shown to make better predictions than more complex rules, especially when the future values of a criterion are highly uncertain. In this article, we provide evidence that some of the fast and frugal heuristics that people use intuitively are able to make forecasts that are as good as or better than those of knowledge-intensive procedures. We draw from research on the adaptive toolbox and ecological rationality to demonstrate the power of using intuitive heuristics for forecasting in various domains including sport, business, and crime.

Suggested Citation

  • Goldstein, Daniel G. & Gigerenzer, Gerd, 2009. "Fast and frugal forecasting," International Journal of Forecasting, Elsevier, vol. 25(4), pages 760-772, October.
    • Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:760-772
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    References listed on IDEAS

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    1. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    2. MacKinlay, A Craig & Pastor, Lubos, 2000. "Asset Pricing Models: Implications for Expected Returns and Portfolio Selection," The Review of Financial Studies, Society for Financial Studies, vol. 13(4), pages 883-916.
    3. Fildes, Robert & Nikolopoulos, Konstantinos, 2006. "Spyros Makridakis: An interview with the International Journal of Forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 625-636.
    4. Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 621-656, September.
    5. Boulier, Bryan L. & Stekler, H. O., 2003. "Predicting the outcomes of National Football League games," International Journal of Forecasting, Elsevier, vol. 19(2), pages 257-270.
    6. Andersson, Patric & Edman, Jan & Ekman, Mattias, 2005. "Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts," International Journal of Forecasting, Elsevier, vol. 21(3), pages 565-576.
    7. Boulier, Bryan L. & Stekler, H. O., 1999. "Are sports seedings good predictors?: an evaluation," International Journal of Forecasting, Elsevier, vol. 15(1), pages 83-91, February.
    8. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    9. Lorenzo Garlappi & Raman Uppal & Tan Wang, 2007. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," The Review of Financial Studies, Society for Financial Studies, vol. 20(1), pages 41-81, January.
    10. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    11. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    12. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
    13. Ľuboš Pástor, 2000. "Portfolio Selection and Asset Pricing Models," Journal of Finance, American Finance Association, vol. 55(1), pages 179-223, February.
    14. Laura Martignon & Ulrich Hoffrage, 2002. "Fast, frugal, and fit: Simple heuristics for paired comparison," Theory and Decision, Springer, vol. 52(1), pages 29-71, February.
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