Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping

Mathew D. Hardy, Sam Zhang, Jessica Hullman, Jake M. Hofman and Daniel Goldstein

International Journal of Forecasting, 2025, vol. 41, issue 2, 689-701

Abstract: We propose and test a method for out-of-population prediction termed model-assisted judgmental bootstrapping, which leverages a predictive model from one domain combined with expert judgment to generate training data and subsequently a predictive model for a new domain. In a preregistered experiment (N=1440), we assessed the predictive accuracy of this method in increasingly challenging environments. We also analyzed the individual contributions of two techniques that underlie the method: model-assisted estimation and judgmental bootstrapping. Our findings revealed that both techniques significantly improved predictive accuracy. Furthermore, their impacts were complementary: model-assisted estimation provided the largest accuracy gains in the least demanding environment, while judgmental bootstrapping did so in the most challenging environment. Our results suggest that model-assisted judgmental bootstrapping is a promising technique for creating predictive models in domains in which outcome data are not available.

Keywords: Judgemental forecasting; Bootstrapping; Adjusting forecasts; Decision making; Time Series; Model Selection (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:2:p:689-701

DOI: 10.1016/j.ijforecast.2024.07.002

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