Dynamics of expert adjustment to model-based forecast

Experts often add domain knowledge to model-based forecasts while aiming to reduce forecast errors. Indeed, there is some empirical evidence that expert-adjusted forecasts improve forecast quality. However, surprisingly little is known about what experts actually do. Based on a large and detailed da...

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Bibliographic Details
Published inIDEAS Working Paper Series from RePEc
Main Authors Franses, PhHBF, Legerstee, R
Format Paper
LanguageEnglish
Published St. Louis Federal Reserve Bank of St. Louis 01.01.2007
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Summary:Experts often add domain knowledge to model-based forecasts while aiming to reduce forecast errors. Indeed, there is some empirical evidence that expert-adjusted forecasts improve forecast quality. However, surprisingly little is known about what experts actually do. Based on a large and detailed database concerning monthly pharmaceutical sales forecasts, we examine whether expert adjustment is predictable. We find substantial evidence that the size, the relative size and even the sign of such adjustment show positive-valued dynamics. The main drivers of current expert adjustments are past adjustment and past model-based forecast errors. Our findings are also that experts' adjustment may suffer from double counting and that trust in the statistical model is not large. An implication is that models may need improvement. Also, experts need to focus on other variables than past sales data when adjusting model-based forecasts. Finally, the method to evaluate the quality of experts' adjustment needs to be modified.