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.

本文提供了经验证据，证明了判断调整如何影响宏观经济密度预测的准确性。判断的定义是专业预测者的密度与统计模型的预测密度之差。使用熵倾斜，我们评估有关均值，方差和偏斜的判断是否提高了英国产出增长和通货膨胀率密度预测的准确性。我们发现并非所有的判断性调整都会有所帮助。关于点预测的判断倾向于在短期内以及在宏观经济不确定性加剧的情况下提高密度预测的准确性。关于方差的判断会在较短的时间范围内有所阻碍，但可以改善较长的时间范围内的尾部风险预测。一般而言，有关偏斜的判断会失去价值，当统计模型花了更长的时间才知道随着大萧条的结束，下行风险已经降低时，收益才可以用于更长的水平产出增长预测。总体而言，来自统计模型的密度预测证明是无与伦比的。