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Heterogeneous Forecast Adjustment

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  • Bert De Bruijn
  • Philip Hans Franses

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.

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  • Bert De Bruijn & Philip Hans Franses, 2017. "Heterogeneous Forecast Adjustment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(4), pages 337-344, July.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:4:p:337-344
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    Cited by:

    1. Etienne Theising, 2024. "Distributional Reference Class Forecasting of Corporate Sales Growth With Multiple Reference Variables," Papers 2405.03402, arXiv.org.
    2. Etienne Theising & Dominik Wied & Daniel Ziggel, 2023. "Reference class selection in similarity‐based forecasting of corporate sales growth," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1069-1085, August.

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