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Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model

Author

Listed:
  • Clark, Todd
  • Huber, Florian
  • Koop, Gary
  • Marcellino, Massimiliano
  • Pfarrhofer, Michael

Abstract

We develop a non-parametric quantile panel regression model. Within each quantile, the response function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroskedastic latent factor. The non-parametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop Bayesian methods for inference and apply several versions of the model to study growth-at-risk dynamics in a panel of 11 advanced economies. Our framework usually improves upon single-country quantile models in recursive growth forecast comparisons.

Suggested Citation

  • Clark, Todd & Huber, Florian & Koop, Gary & Marcellino, Massimiliano & Pfarrhofer, Michael, 2023. "Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model," CEPR Discussion Papers 18549, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:18549
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    2. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    3. Dimitris Korobilis & Maximilian Schröder, 2025. "Probabilistic Quantile Factor Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(3), pages 530-543, July.
    4. Simon Lloyd & Ed Manuel & Konstantin Panchev, 2024. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(1), pages 335-392, March.
    5. Maximilian Boeck & Michael Pfarrhofer, 2025. "Belief Shocks and Implications of Expectations About Growth‐at‐Risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 341-348, April.
    6. Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024. "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(3), pages 339-366, November.
    7. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    8. Ignace De Vos & Gerdie Everaert, 2025. "GLS Estimation of Local Projections: Trading Robustness for Efficiency," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 24/1095, Ghent University, Faculty of Economics and Business Administration.
    9. Tobias Adrian & Hongqi Chen & Max-Sebastian Dov`i & Ji Hyung Lee, 2025. "Machine-learning Growth at Risk," Papers 2506.00572, arXiv.org.
    10. Andrey Polbin & Andrei Shumilov, 2025. "Nowcasting and forecasting Russian GDP and its components using quantile models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 5-26.
    11. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Machine learning the macroeconomic effects of financial shocks," Economics Letters, Elsevier, vol. 250(C).
    12. Lv, Mengdi & Jiao, Shoukun & Ye, Shiqi & Song, Hongmei & Xu, Jiexin & Ye, Wuyi, 2024. "Assessing time-varying risk in China’s GDP growth," Economics Letters, Elsevier, vol. 242(C).
    13. Korobilis, Dimitris & Schröder, Maximilian, 2025. "Monitoring multi-country macroeconomic risk: A quantile factor-augmented vector autoregressive (QFAVAR) approach," Journal of Econometrics, Elsevier, vol. 249(PC).
    14. Ramsey, A. Ford & Ghosh, Sujit K., 2025. "Bayesian Additive Regression Tree (BART) Models of Market Integration in the 19th-Century Trans-Atlantic Wheat Trade," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 361103, Agricultural and Applied Economics Association.
    15. Polbin, Andrey & Shumilov, Andrei, 2025. "Наукастинг И Прогнозирование Ввп России И Его Компонентов С Помощью Квантильных Моделей [Nowcasting and forecasting Russian GDP and its components using quantile models]," MPRA Paper 125440, University Library of Munich, Germany.
    16. Vegard Høghaug Larsen & Nicolò Maffei-Faccioli & Laura Pagenhardt, 2025. "Where do they care? The ECB in the media and inflation expectations," Applied Economics Letters, Taylor & Francis Journals, vol. 32(7), pages 945-950, April.
    17. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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