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Measuring and adjusting for overconfidence

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  • P. Schanbacher

Abstract

To evaluate density forecasts, the applied scoring rule is often arbitrarily chosen. The selection of the scoring rule strongly influences the ranking of forecasts. This paper identifies overconfidence as the main driver for scoring differences. A novel approach to measure overconfidence is proposed. Based on a non-proper scoring rule, the forecasts can be individually adjusted toward a calibrated forecast. Applying the adjustment procedure to the survey of professional forecasters, it can be shown that out-of-sample forecasts can be significantly improved. Also the ranking of the adjusted forecasts becomes less sensitive to the selection of scoring rules. Copyright Springer-Verlag Italia 2014

Suggested Citation

  • P. Schanbacher, 2014. "Measuring and adjusting for overconfidence," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 423-452, October.
    • Handle: RePEc:spr:decfin:v:37:y:2014:i:2:p:423-452
    DOI: 10.1007/s10203-013-0153-y
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    More about this item

    Keywords

    Belief measurement; Proper scoring rules; Overconfidence; Probability adjustment; C53; E37; D81;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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