Professional Forecasters vs. Shallow Neural Network Ensembles: Assessing Inflation Prediction Accuracy

Jane M. Binner (), Logan J. Kelly and Jonathan A. Tepper
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Jane M. Binner: Birmingham Business School, University of Birmingham, Birmingham B15 2TY, UK
Logan J. Kelly: School of Business and Economics, University of Wisconsin, River Falls, WI 54022, USA
Jonathan A. Tepper: Perceptronix Limited, Derby DE65 5AE, UK

JRFM, 2025, vol. 18, issue 4, 1-16

Abstract: Accurate inflation forecasting is crucial for effective monetary policy, particularly during turning points that demand policy realignment. This study examines the efficacy of dedicating ensembles of shallow recurrent neural network models to different forecasting horizons for predicting U.S. inflation turning points more precisely than traditional methods, including the Survey of Professional Forecasters (SPF). We employ monthly data from January 1970 to May 2024, training these ensemble models on information through December 2022 and testing on out-of-sample observations from January 2023 to May 2024. The models generate forecasts at horizons of up to 16 months (one ensemble per horizon), accounting for both short- and medium-term dynamics. The results indicate that such ensembles of recurrent neural networks consistently outperform conventional approaches using key performance metrics, notably detecting inflation turning points earlier and projecting a return to target levels by May 2024—several months ahead of the Survey of Professional Forecasters’ average forecast. These findings underscore the value of such ensembles in capturing complex nonlinear relationships within macroeconomic data, offering a more robust alternative to standard econometric methods. By delivering timely and accurate forecasts, dedicated ensembles of shallow recurrent neural networks hold great promise for informing proactive policy measures and guiding decisions under uncertain economic conditions.

Keywords: inflation forecasting; shallow neural network ensembles; nonlinear modeling; machine learning; out-of-sample prediction; survey of professional forecasters (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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