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. Copyright © 2016 John Wiley & Sons, Ltd.

Reference class selection in similarity‐based forecasting of corporate sales growth

“…By means of this objective data set, the forecaster becomes empowered to challenge and improve his forecast (Kahneman & Tversky, 1979). Adjusting or correcting forecasts is an already established tool in the financial and forecasting literature in terms of judgementally adjusting model‐based forecasts by experts (Wolfe & Flores, 1990; Sanders & Ritzman, 2001; De Bruijn & Franses, 2017), combining statistical forecasts with analysts' predictions (Lobo, 1991; Bunn & Wright, 1991) and combining analysts' forecasts or using consensus forecasts (Butler & Saraoglu, 1999; Ramnath et al, 2005; Jame et al, 2016). However, Du and McEnroe (2011) examine reports by research firms with multiple analysts' forecasts.…”

“…By means of this objective data subset of similar firms the forecaster is equipped to challenge and improve their forecast (Kahneman and Tversky, 1979). Such adjustements of model based forecasts by experts (Wolfe and Flores, 1990;Sanders and Ritzman, 2001;De Bruijn and Franses, 2017) or combinations of statistical forecasts with analysts' predictions (Lobo, 1991;Bunn and Wright, 1991) are already established in the financial and forecasting literature. Additionally, the resulting reference classes can be used to issue a distributional forecast and interval or point forecasts.…”