Forecasting by Cross-Sectional Aggregation

Giulio Zotteri, Matteo Kalchschmidt and Nicola Saccani

Foresight: The International Journal of Applied Forecasting, 2014, issue 35, 35-41

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

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:for:ijafaa:y:2014:i:35:p:35-41

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