Maximizing Forecast Value Added through Machine Learning and "Nudges"

Jeff Baker

Foresight: The International Journal of Applied Forecasting, 2021, issue 60, 8-15

Abstract: We know that manual adjustments of statistical forecasts can fail to improve accuracy by any significant degree and frequently even make forecasts less accurate. It is therefore, in the forecaster's interest to limit adjustments to those likely to provide meaningful accuracy improvements. In this article, Jeff Baker introduces the notion of a threshold level of forecast value added (FVA) to delineate beneficial from damaging overrides to statistical forecasts. He then presents a model to predict FVA from the characteristics of the override and recommends use of the "nudge" to influence how stakeholders view and implement manual overrides. Copyright International Institute of Forecasters, 2021

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:for:ijafaa:y:2021:i:60:p:8-15

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