All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments

Abstract

This paper examines data from the European Central Bank’s Survey of Professional Forecasters to investigate whether participants display equal predictive performance. We use panel data models to evaluate point- and density-based forecasts of real GDP growth, inflation, and unemployment. The results document systematic differences in participants’ forecast accuracy that are not time invariant, but instead vary with the difficulty of the forecasting environment. Specifically, we find that some participants display higher relative accuracy in tranquil environments, while others display higher relative accuracy in volatile environments. We also find that predictive performance is positively correlated across target variables and horizons, with density forecasts generating stronger correlation patterns. Taken together, the results support the development of expectations models featuring persistent heterogeneity.