Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model
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.
Keywords
Cite
@article{arxiv.2110.03411,
title = {Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model},
author = {Todd E. Clark and Florian Huber and Gary Koop and Massimiliano Marcellino and Michael Pfarrhofer},
journal= {arXiv preprint arXiv:2110.03411},
year = {2021}
}
Comments
JEL: C11, C32, C53; Keywords: non-parametric regression, regression trees, forecasting