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Using deep (machine) learning to forecast US inflation in the COVID‐19 era

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  • David Stoneman
  • John V. Duca

Abstract

The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long‐term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID‐19–specific data and discuss some implications of our findings for economic forecasting in global crises.

Suggested Citation

  • David Stoneman & John V. Duca, 2024. "Using deep (machine) learning to forecast US inflation in the COVID‐19 era," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 894-902, July.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:4:p:894-902
    DOI: 10.1002/for.3079
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    References listed on IDEAS

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