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Does judgment improve macroeconomic density forecasts?
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DOI：10.1016/j.ijforecast.2021.02.007.png)
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Abstract
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This paper presents empirical evidence on how judgmental adjustments affect the accuracy of macroeconomic density forecasts. Judgment is defined as the difference between professional forecasters' densities and the forecast densities from statistical models. Using entropic tilting, we evaluate whether judgments about the mean, variance and skew improve the accuracy of density forecasts for UK output growth and inflation. We find that not all judgmental adjustments help. Judgments about point forecasts tend to improve density forecast accuracy at short horizons and at times of heightened macroeconomic uncertainty. Judgments about the variance hinder at short horizons, but can improve tail risk forecasts at longer horizons. Judgments about skew in general take value away, with gains seen only for longer horizon output growth forecasts when statistical models took longer to learn that downside risks had reduced with the end of the Great Recession. Overall, density forecasts from statistical models prove hard to beat. (C) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Judgment forecasting
Density forecasting
Skewness
Exponential tilting
Forecasting uncertainty
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