标题： 预测气候政策不确定性：来自美国的证据 显示英文标题

标题： Forecasting Climate Policy Uncertainty: Evidence from the United States

摘要： 预测气候政策不确定性（CPU）至关重要，因为政策制定者努力在经济增长与环境目标之间取得平衡。 高水平的CPU会减缓对绿色技术的投资，使监管规划更加困难，并在经济压力时期增加公众对气候改革的抵触情绪。 本研究通过构建包含大量协变量的贝叶斯结构时间序列（BSTS）模型来解决预测美国CPU指数的挑战，这些协变量包括经济指标、金融周期数据以及通过Google趋势捕捉的公众情绪。 BSTS模型的关键优势在于其基于尖峰-平滑先验的动态特征选择机制，能够高效处理大量协变量。 为了验证BSTS模型所选特征的有效性，进行了脉冲响应分析。 结果表明，宏观金融冲击在不同时期以不同的方式影响CPU。 进行了数值实验，以评估BSTS模型在不同预测时间范围内的美国CPU数据集上的性能。 实证结果证实，BSTS在半长期和长期预测中始终优于经典方法和深度学习框架。 显示英文摘要

摘要： Forecasting Climate Policy Uncertainty (CPU) is essential as policymakers strive to balance economic growth with environmental goals. High levels of CPU can slow down investments in green technologies, make regulatory planning more difficult, and increase public resistance to climate reforms, especially during times of economic stress. This study addresses the challenge of forecasting the US CPU index by building the Bayesian Structural Time Series (BSTS) model with a large set of covariates, including economic indicators, financial cycle data, and public sentiments captured through Google Trends. The key strength of the BSTS model lies in its ability to efficiently manage a large number of covariates through its dynamic feature selection mechanism based on the spike-and-slab prior. To validate the effectiveness of the selected features of the BSTS model, an impulse response analysis is performed. The results show that macro-financial shocks impact CPU in different ways over time. Numerical experiments are performed to evaluate the performance of the BSTS model with exogenous variables on the US CPU dataset over different forecasting horizons. The empirical results confirm that BSTS consistently outperforms classical and deep learning frameworks, particularly for semi-long-term and long-term forecasts.