贝叶斯动态分位数模型平均
Bayesian Dynamic Quantile Model Averaging
AI总结：
本文提出一种动态贝叶斯模型平均框架，结合序贯马尔可夫链蒙特卡洛方法处理时变参数分位数回归，通过模拟和实证验证，揭示通货膨胀和房价决定因素的时变性，为快速变化环境中的决策提供见解。
摘要：
本文介绍了一种新颖的动态框架，用于时变参数分位数回归的贝叶斯模型平均。通过采用序贯马尔可夫链蒙特卡洛方法，我们将从动态选择的分位数回归中得出的经验估计相结合，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模拟数据集的检验得到验证，随后通过两个应用——美国通货膨胀率和美国房地产市场——得到进一步验证。我们的实证结果表明，在考察不同子时期制度时，需要进行更复杂和细致的分析，因为通货膨胀和房地产价格的决定因素明显是时变的。总之，我们建议我们提出的方法可以为快速变化环境中的决策提供有价值的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
