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Forecasting euro area inflation with machine-learning models

Author

Listed:
  • Lenza, Michele
  • Moutachaker, Inès
  • Paredes, Joan

Abstract

Inflation forecasts and their risks are key for monetary policy decisions. The strategy review concluded in 2021 highlighted how most Eurosystem models used to forecast inflation are linear. Linear models assume that changes in, for example, wages, always have the same fixed, proportional effect on inflation. A new machine learning model, recently developed at the ECB, captures very general forms of non-linearity, such as a changing sensitivity of inflation dynamics to prevailing economic circumstances. Forecasts from this machine learning model closely track Eurosystem staff inflation projections, suggesting that these projections capture mild non-linearity in inflation dynamics – likely owing to expert judgement – and are in line with state-of-the-art econometric methodologies. JEL Classification: C52, C53, E31, E37

Suggested Citation

  • Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, vol. 112.
  • Handle: RePEc:ecb:ecbrbu:2023:0112:
    Note: 411196
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    Inflation; Non-linearity; Quantile Regression Forest;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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