2021/03/05 by Michael Pfarrhofer, Pfarrhofer, Michael
Economics, Econometrics and Finance · #Applications (stat.AP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Italy: Economic History and Contemporary Issues #Market Dynamics and Volatility #Monetary Policy and Economic Impact
paper · pdf · doi:10.48550/arxiv.2103.03632
openalex publication_date 2021/03/05 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.