IMF Working Papers

Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning

By Yang Liu, Ran Pan, Rui Xu

September 27, 2024

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Format: Chicago

Yang Liu, Ran Pan, and Rui Xu. "Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning", IMF Working Papers 2024, 206 (2024), accessed September 27, 2024, https://0-doi-org.library.svsu.edu/10.5089/9798400285387.001

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Disclaimer: 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.

Summary

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.

Subject: Consumer price indexes, Econometric analysis, Econometric models, Economic forecasting, Exchange rates, Foreign exchange, Inflation, Inflation persistence, Labor, Machine learning, Output gap, Prices, Producer price indexes, Production, Technology, Unemployment rate

Keywords: Central America, Comprehensive surveillance review, Consumer price indexes, Core inflation, COVID-19, Econometric analysis, Econometric models, Exchange rates, Forecasting, Inflation, Inflation persistence, Japan, LASSO, Lebanon, Machine learning, Machine learning models, Output gap, Producer price indexes, South Africa, Unemployment rate

Publication Details

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