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.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2021
- DOI:
- 10.48550/arXiv.2110.03411
- arXiv:
- arXiv:2110.03411
- Bibcode:
- 2021arXiv211003411C
- Keywords:
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- Economics - Econometrics;
- Statistics - Applications
- E-Print:
- JEL: C11, C32, C53