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Forecasting by Cross-Sectional Aggregation

Author

Listed:
  • Giulio Zotteri
  • Matteo Kalchschmidt
  • Nicola Saccani

Abstract

Rather than automatically proceeding to forecast with data at the same level of aggregation as that required for an organizationÕs operations, the authors explain that the best level of aggregation for forecasting should be chosen by the forecasters in consideration of the trade-off between sampling error (data inadequate to generate reliable forecasts) and specification error (data too aggregated to represent diverse demands). Doing so frees the forecaster from an unneeded constraint, thus opening new opportunities to improve forecasting performance. Copyright International Institute of Forecasters, 2014

Suggested Citation

  • Giulio Zotteri & Matteo Kalchschmidt & Nicola Saccani, 2014. "Forecasting by Cross-Sectional Aggregation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 35, pages 35-41, Fall.
    • Handle: RePEc:for:ijafaa:y:2014:i:35:p:35-41
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    Cited by:

    1. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    2. Tom Wilson, 2016. "Evaluation of Alternative Cohort-Component Models for Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(2), pages 241-261, April.

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