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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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    Cited by:

    1. Dimitris Korobilis & Maximilian Schroder, 2022. "Probabilistic Quantile Factor Analysis," Papers 2212.10301, arXiv.org, revised Aug 2024.
    2. 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.
    3. Dimitris Korobilis & Maximilian Schroder, 2023. "Monitoring multicountry macroeconomic risk," Papers 2305.09563, arXiv.org.
    4. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    5. Ignace De Vos & Gerdie Everaert, 2024. "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.
    6. Vegard Høghaug Larsen & Nicolò Maffei-Faccioli & Laura Pagenhardt, 2023. "Where do they care? The ECB in the media and inflation expectations," Working Papers No 04/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

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    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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