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Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning

Yang LiuRan PanRui Xu

出版	International Monetary Fund, 2024-09-27
ISBN	9798400285387
URL	http://books.google.com.hk/books?id=ayMmEQAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

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.