This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regression (QR) models featuring conditional heteroskedasticity. I use data augmentation schemes to render the model conditionally Gaussian and develop an efficient Gibbs sampling algorithm. Regularization of the high-dimensional parameter space is achieved via flexible dynamic shrinkage priors. A simple version of TVP-QR based on an unobserved component model is applied to dynamically trace the quantiles of the distribution of inflation in the United States, the United Kingdom and the euro area. In an out-of-sample forecast exercise, I find the proposed model to be competitive and perform particularly well for higher-order and tail forecasts. A detailed analysis of the resulting predictive distributions reveals that they are sometimes skewed and occasionally feature heavy tails.
翻译:本文建议了巴伊西亚参数(TVP)四分位回归模型(QR)的时间变数参数(TVP)的推论方法,这些模型具有有条件的四舍五入性。我使用数据增强方案使模型有条件地成为高森模型,并开发一个高效的Gibs抽样算法。高维参数空间的正规化是通过灵活的动态缩小预科实现的。基于未观测的元件模型的简单版本的TVP-QR用于动态跟踪美国、联合王国和欧元区通货膨胀分布的四分位数。在模拟外的预测活动中,我发现拟议的模型具有竞争力,在更高级和尾部预测方面表现得特别好。对由此产生的预测分布进行的详细分析显示,这些模型有时被扭曲,有时有重尾部。