AI 中文总结
该研究提出软非交叉贝叶斯面板分位数回归模型，应用于33国1979-2023年数据，发现全球温度冲击会给产出增长带来集中于尾部下端的系统性不可分散下行风险，且能将样本外尾部风险预测损失降低约三分之一。
AI 中文摘要
我们开发了一种分层贝叶斯面板分位数回归模型，其中特定单元的系数路径通过高斯过程在分位数间平滑，而共同时间效应则吸收总体冲击。受特定单元偏差扰动的分量单调伯恩斯坦多项式实现软非交叉，我们提供了识别条件以及交叉概率的界限。将该模型应用于1979-2023年间的33个国家，我们发现全球温度冲击会对产出增长产生系统性、不可分散的下行风险，该风险集中在尾部下端，且对新兴市场的影响不成比例。最后，我们将该框架应用于风险分析，结果显示，与特定国家的分位数回归相比，该模型将样本外尾部风险预测损失降低了约三分之一。
英文摘要
We develop a hierarchical Bayesian panel quantile regression model in which unit-specific coefficient paths are smoothed across quantiles by Gaussian processes, while a common time effect absorbs aggregate shocks. Componentwise-monotone Bernstein polynomials, perturbed by unit-specific deviations, deliver soft noncrossing, and we provide identification conditions together with a bound on the crossing probability. Applying the model to 33 countries over 1979--2023, we find that global temperature shocks generate a systemic, non-diversifiable downside risk to output growth. This risk is concentrated in the lower tail and disproportionately affects emerging markets. Finally, we apply our framework to risk analysis and show that the model reduces out-of-sample tail-risk forecast loss by roughly one-third relative to country-specific quantile regressions.
