Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model

Todd Clark, Florian Huber, Gary Koop, Massimiliano Marcellino and Michael Pfarrhofer

Journal of Business & Economic Statistics, 2024, vol. 42, issue 4, 1302-1317

Abstract: We develop a nonparametric quantile panel regression model. Within each quantile, the quantile 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 heteroscedastic latent factor. The nonparametric 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.

Date: 2024
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Related works:
Working Paper: Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model (2023)
Working Paper: Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model (2021)
Working Paper: Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model (2021)
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DOI: 10.1080/07350015.2024.2310020

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