Modeling tail risks of inflation using unobserved component quantile regressions
- Author(s)
- Michael Pfarrhofer
- Abstract
This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regressions (QRs) featuring conditional heteroskedasticity. I 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, and 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, 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.
- Organisation(s)
- External organisation(s)
- Paris-Lodron Universität Salzburg
- Journal
- Journal of Economic Dynamics and Control
- Volume
- 143
- No. of pages
- 19
- ISSN
- 0165-1889
- DOI
- https://doi.org/10.1016/j.jedc.2022.104493
- Publication date
- 07-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502025 Econometrics, 502018 Macroeconomics
- Keywords
- ASJC Scopus subject areas
- Control and Optimization, Applied Mathematics, Economics and Econometrics
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/modeling-tail-risks-of-inflation-using-unobserved-component-quantile-regressions(ded9d98b-67e8-447b-867f-09987a041e34).html