Bayesian Dynamic Quantile Model Averaging (1602.00856v2)

Abstract: This article introduces a novel dynamic framework to Bayesian model averaging for time-varying parameter quantile regressions. By employing sequential Markov chain Monte Carlo, we combine empirical estimates derived from dynamically chosen quantile regressions, thereby facilitating a comprehensive understanding of the quantile model instabilities. The effectiveness of our methodology is initially validated through the examination of simulated datasets and, subsequently, by two applications to the US inflation rates and to the US real estate market. Our empirical findings suggest that a more intricate and nuanced analysis is needed when examining different sub-period regimes, since the determinants of inflation and real estate prices are clearly shown to be time-varying. In conclusion, we suggest that our proposed approach could offer valuable insights to aid decision making in a rapidly changing environment.