Experts' adjustment to model-based forecasts: Does the forecast horizon matter?
Experts may have domain-specific knowledge that is not included in a statistical model and that can improve forecasts. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional mean of a time series variable. This suggests that added expertise could be most beneficial to forecast quality for immediate horizons (as very recent events are not in the model) and for further away horizons (as they may miss foreseen trends), and less so for intermediate horizons. Relying on a huge database concerning pharmaceutical sales forecasts for various products and adjusted by a range of experts, we examine and verify this and other conjectures. We also document that the forecast horizon that is the most important for supply chain management here entails the heaviest adjustment by the experts. Unfortunately, that additional adjustment harms forecast accuracy.
|forecasting, human judgement and decision-making, performance measurement|
|Erasmus School of Economics|
|Econometric Institute Research Papers|
|Report / Econometric Institute, Erasmus University Rotterdam|
|Organisation||Erasmus School of Economics|
Franses, Ph.H.B.F, & Legerstee, R. (2007). Experts' adjustment to model-based forecasts: Does the forecast horizon matter? (No. EI 2007-51). Report / Econometric Institute, Erasmus University Rotterdam. Erasmus School of Economics. Retrieved from http://hdl.handle.net/1765/10876