Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning

Yang Liu, Ran Pan and Rui Xu

No 2024/206, IMF Working Papers from International Monetary Fund

Abstract: Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting performance by incorporating a wider range of variables, allowing for non-linear relationships, and focusing on out-of-sample performance. In this paper, we apply machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because inflation had been muted until 2022 and has now risen to a level not seen in four decades. Four machine learning models are applied to a large set of predictors alongside two benchmark models. For 2023, the two penalized regression models systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post-2022 include household inflation expectations, inbound tourism, exchange rates, and the output gap.

Keywords: Core inflation; forecasting; machine learning models; LASSO; Japan; household inflation expectation; forecasting inflation; machine learning method; Annex I. machine learning; enhanced inflation; Inflation; Machine learning; Econometric models; Output gap (search for similar items in EconPapers)
Pages: 23
Date: 2024-09-27
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-for and nep-mon
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