The benefits of forecasting inflation with machine learning

New evidence

Bibliographic Data

ID21653614
AuthorsAndrea A Naghi (Erasmus University Rotterdam Rotterdam The Netherlands, corresponding author), Eoghan O’Neill (0000-0002-1274-4248, Queen Mary University of London London UK), Martina Danielova Zaharieva (0000-0002-5776-7013, CUNEF University Madrid Spain)
Year2024
Volume39
Issue7
Pages1321-1331
Publication date2024-11-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.3088
OpenAlexW4401428459
LanguageEN
Citations received2
References cited29

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

Econometrics · Economics · Gradient boosting · Machine learning · Physics · Random forest · Replicate · Statistics · Computer Science · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact · Stock Market Forecasting Methods · Artificial Intelligence

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