Strategic bias and popularity effect in the prediction of economic surprises
Félix, L., Kräussl, R. ORCID: 0000-0001-8933-9278 & Stork, P. (2021). Strategic bias and popularity effect in the prediction of economic surprises. Journal of Forecasting, 40(6), pp. 1095-1117. doi: 10.1002/for.2764
Abstract
Professional forecasters of economic data are remunerated based on accuracy and positive publicity generated for their firms. This remuneration structure incentivizes them to stick to the median forecast but also to make bold forecasts when they perceive to have superior private information. We find that skewness in the distribution of expectations, potentially created by bold forecasts, predicts economic surprises across a wide range of US economic indicators in‐sample and out‐of‐sample, confirming our hypothesis that forecasters behave strategically and possess private information. This strategic bias found in US economic forecasts is also exhibited in individual forecasters' data as well as in continental Europe, the United Kingdom, and Japan. We show that it has been increasing both through time and in relation to the behavioral anchor bias. Our results suggest that the pervasiveness of the biases depends on the popularity of the economic indicator being released, both in the United States and internationally.
Publication Type: | Article |
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Additional Information: | © 2021 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
Publisher Keywords: | economic surprises, forecast error, predictability, skewness, strategic bias |
Subjects: | H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory |
Departments: | Bayes Business School Bayes Business School > Finance |
SWORD Depositor: |
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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