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A1760
Title: Investigating growth at risk using a multi-country non-parametric quantile factor model (virtual) Authors:  Todd Clark - Federal Reserve Bank of Cleveland (United States)
Florian Huber - University of Salzburg (Austria)
Gary Koop - University of Strathclyde (United Kingdom) [presenting]
M. Marcellino - Bocconi University (Italy)
Michael Pfarrhofer - University of Salzburg (Austria)
Abstract: A Bayesian non-parametric quantile panel regression model is developed. 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 $p^{th}$ 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