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The benefits of forecasting inflation with machine learning: New evidence

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  • Andrea A. Naghi
  • Eoghan O'Neill
  • Martina Danielova Zaharieva

Abstract

Medeiros et al. (2021) (Journal of Business & Economic Statistics, 39:1, 98–119) find that random forest (RF) outperforms US inflation forecasting benchmarks. We replicate the main results in Medeiros et al. (2021) and (1) considerably expand the set of machine learning methods, (2) analyse the predictive ability of both the initial and extended sets of methods on Canadian and UK data, (3) add results on coverage rates and widths of prediction intervals and (4) extend the sample from January 2016 to October 2022. Our narrow replication confirms the main findings of the original paper. However, the wider replication results suggest that other methods are competitive with RF and often more accurate. In addition, RF produces disappointing results during the coronavirus pandemic and subsequent high inflation of 2020–2022, whereas a stochastic volatility model and some gradient boosting methods produce more accurate forecasts.

Suggested Citation

  • Andrea A. Naghi & Eoghan O'Neill & Martina Danielova Zaharieva, 2024. "The benefits of forecasting inflation with machine learning: New evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1321-1331, November.
    • Handle: RePEc:wly:japmet:v:39:y:2024:i:7:p:1321-1331
    DOI: 10.1002/jae.3088
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    References listed on IDEAS

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    Cited by:

    1. Faria, Gonçalo & Verona, Fabio, 2024. "Enhancing forecast accuracy through frequencydomain combination: Applications to financial and economic indicators," Bank of Finland Research Discussion Papers 14/2024, Bank of Finland.

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