IMF-修补水晶球：用机器学习增强通胀预测（英）

Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning Yang Liu, Ran Pan and Rui Xu WP/24/206IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 2024 SEP * The author(s) would like to thank Andrii Babii, Chris Erceg, Ichiro Fukunaga, Heedon Kang, Yosuke Kido, seminar participants atthe Bank of Japan, and seminar participants of the MCM Policy Forum for their valuable suggestions and comments. All errorsare our own. © 2024 International Monetary Fund WP/24/206 IMF Working Paper Monetary and Capital Markets Department Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning Prepared by Yang Liu, Ran Pan and Rui Xu* Authorized for distribution by Naomi Nakaguchi Griffin and Ranil Salgado September 2024 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 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. RECOMMENDED CITATION: Liu, Yang, Ran Pan and Rui Xu (2024), “Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning”, IMF Working Paper 24/206. JEL Classification Numbers: E37, C53, C1, C14 Keywords: Core inflation; forecasting; machine learning models; LASSO; Japan Author’s E-Mail Address: YLiu10@imf.org; RPan@imf.org; RXu@imf.org. WORKING PAPERS Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning Prepared by Yang Liu, Ran Pan and Rui Xu 1 1 The author(s) would like to thank Andrii Babii, Chris Erceg, Ichiro Fukunaga, Heedon Kang, Yosuke Kido, seminar participants at the Bank of Japan, and seminar participants of the MCM Policy Forum for their valuable suggestions and comments. All e