Cover IMF Working Papers
Buy Book in Print
Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning
Author:
Yang Liu https://isni.org/isni/0000000404811396 International Monetary Fund

Search for other papers by Yang Liu in
Current site
Google Scholar Close
,
Ran Pan
Search for other papers by Ran Pan in
Current site
Google Scholar Close
, and
Rui Xu https://isni.org/isni/0000000404811396 International Monetary Fund

Search for other papers by Rui Xu in
Current site
Google Scholar Close
Series:
IMF Working Papers
Volume/Issue:
Volume 2024: Issue 206
Language:
English
Publication Date:
27 Sep 2024
DOI:
https://doi.org/10.5089/9798400285387.001
ISBN:
9798400285387
ISSN:
1018-5941
Pages:
23
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
Legal-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.
Download PDF (2.3 MB) Download Epub
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.
  • Collapse
  • Expand
Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund