Disagreement versus Uncertainty: Evidence from Distribution Forecasts

Abstract

We use a cross-section of economic survey forecasts to predict the distribution of US macro variables in real time. This generalizes the existing literature, which uses disagreement (i.e., the cross-sectional variance of survey forecasts) to predict uncertainty (i.e., the conditional variance of future macroeconomic quantities). Our results show that cross-sectional information can be helpful for distribution forecasting, but this information needs to be modelled in a statistically efficient way in order to avoid overfitting. A simple one-parameter model which exploits time variation in the cross-section of survey point forecasts is found to perform well in practice.

Keywords: Disagreement, Uncertainty, Predictive Density, Forecast Combination

JEL Classification: C53, C83, E7, F7