Disagreement versus uncertainty: Evidence from distribution forecasts

Abstract

a b s t r a c tWe 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 modeled 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.

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

“…Our finding that disagreement is of limited help for distribution forecasting differs from the results by Krüger and Nolte (2016), who find that disagreement in US SPF forecasts does have predictive power for some variables and forecast horizons. A possible explanation for this discrepancy is that Krüger and Nolte (2016) consider a longer sampling period, with more variation of disagreement over time.…”

“…Each of these distributions is assumed to have the same variance, θ . The method has been proposed by Krüger and Nolte (2016), who find that it performs well for the US SPF data. The label 'BMA' hints at the method's close conceptual connection to the Bayesian model averaging approach proposed by Raftery et al (2005) in the meteorological forecasting literature.…”

“…Conceptually, the BMA method is based on the idea of fitting a simplistic distribution to each individual forecaster, and then averaging over these distributions. This is why Krüger and Nolte (2016) call it a 'micro-level' method. In contrast, the EMOS method fixes a normal distribution and fits the parameters of that distribution via a summary statistic (the mean) from the ensemble of forecasters.…”

“…A possible explanation for this discrepancy is that Krüger and Nolte (2016) consider a longer sampling period, with more variation of disagreement over time. This may help to identify effects which are hard to identify in our short sample.…”

“…Compared to these studies, the main innovation of the present paper is our use of ensemble methods for constructing forecast distributions. Ensemble methods are used extensively in meteorology (e.g., ; they have received little attention in economics, with the exception of Gneiting and Thorarinsdottir (2010) and Krüger and Nolte (2016) who analyze US SPF data. Furthermore, ensemble methods may account for disagreement in the point forecasts of individual participants.…”