Forecasting Follies: Machine Learning from Human Errors

Li Sun () and Yongchen Zhao
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Li Sun: Department of Business Analytics & Technology Management, Towson University, 8000 York Road, Towson, MD 21252, USA
Yongchen Zhao: Department of Economics, Towson University, Towson, MD 21252, USA

JRFM, 2025, vol. 18, issue 2, 1-25

Abstract: Reliable inflation forecasts are essential for both business operations and macroeconomic policy making. This study explores the potential of using machine learning (ML) techniques to improve the accuracy of human forecasts of inflation. Specifically, we develop and examine ML-centered forecast adjustment procedures where advanced ML techniques are employed to predict and thus mitigate the errors of human forecasts, akin to how an AI-powered spell and grammar checker helps to prevent mistakes in human writing. Our empirical exercises demonstrate the benefits of several popular ML techniques, such as the elastic net, LASSO, and ridge regressions, and provide evidence of their ability to improve both our own benchmark inflation forecasts and those reported by the frequent participants in the US Survey of Professional Forecasters. The forecast adjustment procedures proposed in this paper are conceptually appealing, widely applicable, and empirically effective in reducing forecast bias and improving forecast accuracy.

Keywords: variable selection and regularization; LASSO; ridge regression; elastic net (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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