The Time-Varying Evolution of Inflation Risks

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions ability to flexibly whole distribution inflation. In order make our approach accessible and relevant forecasting, we derive an efficient Gibbs sampler by transforming state-space form into equivalent high-dimensional form. An application this points good performance TVPs augmented specific credit money-based indicators prediction conditional in euro area, both short longer run, specifically tail JEL Classification: C11, C22, C52, C53, C55, E31, E37, E51