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

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

No 2307, Working Papers from University of Strathclyde Business School, Department of Economics

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 exibility, 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

Keywords: non-parametric regression; regression trees; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: pages
Date: 2021-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model (2024)
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)
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