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

Author

Listed:
  • Todd Clark

    (Federal Reserve Bank of Cleveland)

  • Florian Huber

    (University of Salzburg)

  • Gary Koop

    (University of Strathclyde)

  • Massimiliano Marcellino

    (Bocconi University, IGIER and CEPR)

  • Michael Pfarrhofer

    (University of Salzburg)

Abstract

We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear function, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information at the pth quantile is captured through a conditionally heteroscedastic latent factor. The non-parametric feature of our model enhances exibility, while the panel feature, by exploiting cross-country information, increases the number of observations in the tails. We develop Bayesian Markov chain Monte Carlo (MCMC) methods for estimation and forecasting with our quantile factor BART model (QF-BART), and apply them to study growth at risk dynamics in a panel of 11 advanced economies

Suggested Citation

  • Todd Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Working Papers 2307, University of Strathclyde Business School, Department of Economics.
    • Handle: RePEc:str:wpaper:2307
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    Cited by:

    1. Dimitris Korobilis & Maximilian Schroder, 2023. "Monitoring multicountry macroeconomic risk," Papers 2305.09563, arXiv.org.
    2. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    3. Dimitris Korobilis & Maximilian Schroder, 2022. "Probabilistic quantile factor analysis," Papers 2212.10301, arXiv.org, revised Dec 2022.

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    More about this item

    Keywords

    non-parametric regression; regression trees; forecasting;
    All these keywords.

    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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