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We develop a Bayesian non-parametric quantile panel regression model. Withineach quantile, the response function is a convex combination of a linear modeland a non-linear function, which we approximate using Bayesian AdditiveRegression Trees (BART). Cross-sectional information at the pth quantile iscaptured through a conditionally heteroscedastic latent factor. Thenon-parametric feature of our model enhances flexibility, while the panelfeature, by exploiting cross-country information, increases the number ofobservations 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 11advanced economies.

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