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Density forecasts of inflation: a quantile regression forest approach

Author

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

Abstract

Density forecasts of euro area inflation are a fundamental input for a medium-term oriented central bank, such as the European Central Bank (ECB). We show that a quantile regression forest, capturing a general non-linear relationship between euro area (headline and core) inflation and a large set of determinants, is competitive with state-of-the-art linear benchmarks and judgemental survey forecasts. The median forecasts of the quantile regression forest are very collinear with the ECB point inflation forecasts, displaying similar deviations from “linearity”. Given that the ECB modelling toolbox is overwhelmingly linear, this finding suggests that the expert judgement embedded in the ECB forecast may be characterized by some mild non-linearity. JEL Classification: C52, C53, E31, E37

Suggested Citation

  • Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20232830
    Note: 411196
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    1. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2025. "Density forecasts of inflation: A quantile regression forest approach," European Economic Review, Elsevier, vol. 178(C).
    2. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    3. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    4. Michael D. Bauer & Travis J. Berge & Giuseppe Fiori & Francesca Loria & Molin Zhong, 2025. "Accounting for Uncertainty and Risks in Monetary Policy," Finance and Economics Discussion Series 2025-073, Board of Governors of the Federal Reserve System (U.S.).
    5. Saban Nazlioglu & Sinem Pinar Gurel & Sevcan Gunes & Tugba Akin & Cagin Karul & Muhsin Kar, 2025. "Inflation co-movement: new insights from quantile factor model," Empirical Economics, Springer, vol. 69(1), pages 431-464, July.
    6. Bobeica, Elena & Holton, Sarah & Huber, Florian & Martínez Hernández, Catalina, 2025. "Beware of large shocks! A non-parametric structural inflation model," Working Paper Series 3052, European Central Bank.
    7. López-Salido, David & Loria, Francesca, 2024. "Inflation at risk," Journal of Monetary Economics, Elsevier, vol. 145(S).

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    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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