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Density forecasts of inflation: a quantile regression forest approach
[Prévisions de densité de l'inflation : une approche par forêt de régressions quantile]

Author

Listed:
  • Michele Lenza

    (European Central Bank, CEPR - Center for Economic Policy Research)

  • Inès Moutachaker

    (INSEE - Institut national de la statistique et des études économiques (INSEE))

  • Joan Paredes

    (European Central Bank, CEPR - Center for Economic Policy Research)

Abstract

Density forecasts of inflation are a fundamental input for medium-term oriented forecasters, such as National Statistic Institutes or Central Banks. 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 close to the ECB point inflation forecasts, displaying similar deviations from "linearity". Given that the ECB modelling toolbox is essentially linear, this finding suggests that the expert judgement embedded in the ECB forecast may be characterized by some mild non-linearity

Suggested Citation

  • Michele Lenza & Inès Moutachaker & Joan Paredes, 2024. "Density forecasts of inflation: a quantile regression forest approach [Prévisions de densité de l'inflation : une approche par forêt de régressions quantile]," Working Papers hal-05329662, HAL.
    • Handle: RePEc:hal:wpaper:hal-05329662
    Note: View the original document on HAL open archive server: https://insee.hal.science/hal-05329662v1
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    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).

    More about this item

    Keywords

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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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