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.

翻译：本文提出了一种用于时变参数分位数回归的贝叶斯模型平均新动态框架。通过采用序贯马尔可夫链蒙特卡罗方法，我们整合了从动态选择的分位数回归中得到的经验估计，从而促进对分位数模型不稳定性的全面理解。我们方法的有效性首先通过对模拟数据集的检验得到验证，随后通过两个应用案例——美国通货膨胀率和美国房地产市场——进一步证实。我们的实证研究结果表明，在考察不同子时期制度时需要进行更复杂细致的分析，因为通货膨胀和房地产价格的决定因素明显表现出时变性。总之，我们建议所提出的方法可为快速变化环境中的决策制定提供有价值的见解。