A0368
Title: Modeling tail risks of inflation using unobserved component quantile regressions
Authors: Michael Pfarrhofer - University of Vienna (Austria) [presenting]
Abstract: Methods are proposed for Bayesian inference in time-varying parameter (TVP) quantile regressions (QRs) featuring conditional heteroskedasticity. We use data augmentation schemes to render the model conditionally Gaussian and develop an efficient sampling algorithm. Regularization of the high-dimensional parameter space is achieved via dynamic shrinkage priors. The merits of the proposed approach are illustrated in a simulation study. A simple version of TVP-QR based on an unobserved components model is applied to dynamically trace the quantiles of inflation in the United States, the United Kingdom and the euro area. In an out-of-sample forecast exercise, we 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.
