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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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    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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