IDEAS home Printed from https://ideas.repec.org/p/cnb/wpaper/2026-09.html

AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights

Author

Listed:
  • Filip Blaha
  • Jan Botka
  • Josef Sveda
  • Ales Michl

Abstract

We construct a quantile regression forest for inflation forecasting in the Czech Republic, inspired by growing literature on the use of Machine Learning in macroeconomics and finance. We contribute to the literature by implementing an optimisation scheme with time-varying weights that incorporates information from the entire distribution to form the point forecast. By dynamically reflecting the distribution of future inflation paths, our framework outperforms both standard mean and median point forecasts and delivers gains relative to conventional linear benchmark models. We also forecast individual inflation subcomponents that enable us to disentangle the drivers of future inflation and its risks. Furthermore, we integrate the Shapley-value decomposition to enhance the interpretability of our results and adjust the model's predictors for a small open economy.

Suggested Citation

  • Filip Blaha & Jan Botka & Josef Sveda & Ales Michl, 2026. "AI-Based Forecasting of Czech Inflation: Quantile Regression Forests with Dynamic Weights," Working Papers 2026/09, Czech National Bank, Research and Statistics Department.
    • Handle: RePEc:cnb:wpaper:2026/09
    as

    Download full text from publisher

    File URL: https://www.cnb.cz/export/sites/cnb/en/economic-research/.galleries/research_publications/cnb_wp/cnbwp_2026_09.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Kristin J. Forbes & Joseph E. Gagnon & Christopher G. Collins, 2022. "Low Inflation Bends the Phillips Curve around the World," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 45(89), pages 52-72.
    3. Kristin J. Forbes & Joseph E. Gagnon & Christopher G. Collins, 2021. "Low inflation bends the Phillips curve around the world: Extended results," Working Paper Series WP21-15, Peterson Institute for International Economics.
    4. Marcus Buckmann & Andreas Joseph, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
    5. Emanuel Kohlscheen, 2021. "What does machine learning say about the drivers of inflation?," BIS Working Papers 980, Bank for International Settlements.
    6. Figueres, Juan Manuel & Jarociński, Marek, 2020. "Vulnerable growth in the euro area: Measuring the financial conditions," Economics Letters, Elsevier, vol. 191(C).
    7. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    3. James Mitchell & Aubrey Poon & Dan Zhu, 2024. "Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 790-812, August.
    4. Chandler Lester, 2024. "How CBO Projects Inflation," Working Papers 59877, Congressional Budget Office.
    5. Jason Furman, 2022. "Why Did (Almost) No One See the Inflation Coming?," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(2), pages 79-86, March.
    6. Lena Anayi & Nicholas Bloom & Philip Bunn & Paul Mizen & Gregory Thwaites & Ivan Yotzov, 2022. "Firming up price inflation," POID Working Papers 058, Centre for Economic Performance, LSE.
    7. Wellmann, Susanne, 2023. "The Phillips curve in the euro area: New evidence using country-level data," University of Tübingen Working Papers in Business and Economics 156, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    8. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
    9. Andrew Hodge & Zoltan Jakab & Jesper Lindé & Vina Nguyen, 2022. "U.S. and Euro Area Monetary and Fiscal Interactions During the Pandemic: A Structural Analysis," IMF Working Papers 2022/222, International Monetary Fund.
    10. Catherine L. Mann & Lennart Brandt, 2022. "On Returning Inflation to Target," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(2), pages 87-92, March.
    11. Lhuissier, Stéphane, 2022. "Financial conditions and macroeconomic downside risks in the euro area," European Economic Review, Elsevier, vol. 143(C).
    12. De Palma Francesco & Ligonnière Samuel & Saadaoui Jamel & Thommen Yann, 2022. "The role of wage bargaining institutions in the Phillips curve flattening;," Working Papers of BETA 2022-21, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    13. Michau, Jean-Baptiste, 2025. "Fiscal policy under secular stagnation: An optimal pump-priming strategy," Journal of Economic Dynamics and Control, Elsevier, vol. 175(C).
    14. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    15. Erdem Başçı & Sıdıka Başçı, 2021. "Demand Deficiency and Inflation in the G7 Countries," International Econometric Review (IER), Economic Research Association, vol. 13(3), pages 59-70, September.
    16. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    17. Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2025. "Neural network learning for nonlinear economies," Journal of Monetary Economics, Elsevier, vol. 149(C).
    18. Varga, Katalin & Szendrei, Tibor, 2025. "Non-stationary financial risk factors and macroeconomic vulnerability for the UK," International Review of Financial Analysis, Elsevier, vol. 97(C).
    19. Anis Foresto & Monique Reid & Jeffrey Rakgalakane, 2025. "State dependence of the Phillips curve what does this mean for monetary policy," Working Papers 11080, South African Reserve Bank.
    20. Jean-Baptiste Michau, 2022. "The Trilemma for Low Interest Rate Macroeconomics," Working Papers 2022-19, Center for Research in Economics and Statistics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cnb:wpaper:2026/09. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tomas Karhanek (email available below). General contact details of provider: https://edirc.repec.org/data/cnbgvcz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.