Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning

IMF Working Papers, September 27, 2024

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Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic.
Machine learning (ML) models can improve forecasting performance by:
- incorporating a wider range of variables,
- allowing for non-linear relationships,
- focusing on out-of-sample performance.
The paper applies 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.

Models applied:
- Four machine learning models (unspecified in summary text beyond penalized regression and LASSO).
- Two benchmark models.
Evaluation focus:
- Near-term core inflation forecasts for Japan post-pandemic.
- Emphasis on out-of-sample forecasting performance.
Data scope:
- A large set of predictors (details not specified in the summary text).

Penalized regression approaches systematically outperform benchmark models for 2023.
LASSO provides the most accurate forecast among the evaluated models for 2023.
Machine learning methods can enhance inflation forecasting by capturing richer predictor sets and non-linearities and by prioritizing out-of-sample accuracy.

Central banks and forecasters may benefit from incorporating machine learning techniques, particularly penalized regression methods like LASSO, when forecasting inflation in environments with large structural shifts.
Monitoring non-traditional or higher-frequency indicators such as household inflation expectations and inbound tourism can improve near-term inflation signals.
Using broader predictor sets and models that capture non-linear relationships can be valuable in periods of heightened uncertainty and structural change.