Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
提出一个贝叶斯非参数分位数面板回归模型，结合线性模型与BART非线性函数，利用条件异方差潜在因子捕捉截面信息，应用于11个发达经济体的增长风险动态研究。
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.