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arXiv · 2110.03411

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

Todd E. Clark·Florian Huber·Gary Koop·Massimiliano Marcellino·Michael Pfarrhofer
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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 flexibility, 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.

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Todd E. Clark, Florian Huber, Gary Koop, Massimiliano Marcellino, Michael Pfarrhofer. 2021-10-07. Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model. https://arxiv.org/abs/2110.03411

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