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Does Judgment Improve Macroeconomic Density Forecasts?

Ana Galvão (), Anthony Garratt and James Mitchell
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Anthony Garratt: University of Warwick

EMF Research Papers from Economic Modelling and Forecasting Group

Abstract: This paper presents empirical evidence on how judgmental adjustments affect the accuracy of macroeconomic density forecasts. We aim to separate the effects of judgments made about the first three moments of a set of professional forecasters’ density forecasts for UK output growth and inflation. Using entropic tilting methods, we evaluate whether judgmental adjustments about the mean, variance and skewness improve the accuracy of density forecasts from statistical models. We find that not all judgmental adjustments improve density forecasts: overall, density forecasts from statistical models prove hard to beat. Judgments about point forecasts tend to improve density forecast accuracy at short horizons and at times of heightened macroeconomic uncertainty. Judgments about the variance clearly hinder at short horizons, but can help deliver better tail risk forecasts at longer horizons. Finally, 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.

Keywords: density forecasting; judgment forecasting; skewness; exponential tilting; forecasting uncertainty (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-eec, nep-for, nep-mac and nep-ore
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