Modeling tail risks of inflation using unobserved component quantile regressions

TL;DR

This paper introduces a Bayesian TVP-QR model with dynamic shrinkage priors to analyze and forecast inflation tail risks, demonstrating strong out-of-sample performance especially for tail quantiles.

Contribution

It develops an efficient Gibbs sampling approach for TVP-QR models with heteroskedasticity and applies an unobserved component model to inflation data for dynamic tail risk analysis.

Findings

Model performs well in out-of-sample forecasts.

Predictive distributions can be skewed or heavy-tailed.

Tail forecasts are particularly accurate.

Abstract

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.