Heterogeneous forecast adjustment

Abstract

There is ample empirical evidence that expert-adjusted model forecasts can be improved. One way to potential improvement concerns providing various forms of feedback to the sales forecasters. It is also often recognized that the experts (forecasters) might not constitute a homogeneous group. This paper provides a data-based methodology to discern latent clusters of forecasters, and applies it to a fully new large database with data on expert-adjusted forecasts, model forecasts and realizations. For the data at hand, two clusters can clearly be identified. Next, the consequences of having clusters are discussed.