Uncertainty measures from partially rounded probabilistic forecast surveys

Alexander Glas and Matthias Hartmann ()

Quantitative Economics, 2022, vol. 13, issue 3, 979-1022

Abstract: Although survey‐based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Professional forecasters who report inconspicuously low ex ante variances often produce squared forecast errors that are much larger on average. In this paper, we document the novel stylized fact that this variance misalignment is related to the rounding behavior of survey participants. Rounding may reflect the fact that some survey participants employ a rather judgmental approach to forecasting as opposed to using a formal model. We use the distinct numerical accuracies of panelists' reported probabilities as a way to propose several alternative and easily implementable corrections that (i) can be carried out in real time, that is, before outcomes are observed, and (ii) deliver a significantly improved match between ex ante and ex post forecast uncertainty. According to our estimates, uncertainty about inflation, output growth and unemployment in the U.S. and the Euro area is higher after correcting for the rounding effect. The increase in the share of nonrounded responses in recent years also helps to understand the trajectory of survey‐based average uncertainty during the years since the financial and sovereign debt crisis.

Date: 2022
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Citations: View citations in EconPapers (8)

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https://doi.org/10.3982/QE1703

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Working Paper: Uncertainty measures from partially rounded probabilistic forecast surveys (2020)
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