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Monetized Forecast-Error Comparisons

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  • Shaun Snapp

Abstract

Standard error metrics such as the MAPE are not effective in directing a company to make changes that will have the most significant impact. Rather, it is in the monetization of forecast error that we enable the best use of forecasting resources. This article illustrates the calculation of the monetized forecast-error metric and presents the author's free application designed to steer improvement in forecast accuracy at specific product/locations. Copyright International Institute of Forecasters, 2019

Suggested Citation

  • Shaun Snapp, 2019. "Monetized Forecast-Error Comparisons," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 53, pages 33-38, Spring.
    • Handle: RePEc:for:ijafaa:y:2019:i:53:p:33-38
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    Cited by:

    1. Weiqi Zhang & Huong Ha & Hui Ting Evelyn Gay, 2020. "Analysts’ forecasts between last consensus and earning announcement date," Journal of Financial Reporting and Accounting, Emerald Group Publishing Limited, vol. 18(4), pages 779-793, November.

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