Accurate macroeconomic forecasting has become harder amid geopolitical disruptions, policy reversals, and volatile financial markets. Conventional vector autoregressions (VARs) overfit in high dimensional settings, while threshold VARs struggle with time varying interdependencies and complex parameter structures. We address these limitations by extending the Sims Zha Bayesian VAR with exogenous variables (SZBVARx) to incorporate domain-informed shrinkage and four newspaper based uncertainty shocks such as economic policy uncertainty, geopolitical risk, US equity market volatility, and US monetary policy uncertainty. The framework improves structural interpretability, mitigates dimensionality, and imposes empirically guided regularization. Using G7 data, we study spillovers from uncertainty shocks to five core variables (unemployment, real broad effective exchange rates, short term rates, oil prices, and CPI inflation), combining wavelet coherence (time frequency dynamics) with nonlinear local projections (state dependent impulse responses). Out-of-sample results at 12 and 24 month horizons show that SZBVARx outperforms 14 benchmarks, including classical VARs and leading machine learning models, as confirmed by Murphy difference diagrams, multivariate Diebold Mariano tests, and Giacomini White predictability tests. Credible Bayesian prediction intervals deliver robust uncertainty quantification for scenario analysis and risk management. The proposed SZBVARx offers G7 policymakers a transparent, well calibrated tool for modern macroeconomic forecasting under pervasive uncertainty.

翻译：在地缘政治动荡、政策逆转和金融市场剧烈波动的背景下，精准的宏观经济预测变得愈发困难。传统向量自回归模型在高维环境下容易过拟合，而阈值向量自回归模型则难以处理时变相互依赖性和复杂的参数结构。为克服这些局限，本研究扩展了Sims-Zha贝叶斯外生变量向量自回归模型，通过引入领域知识驱动的收缩机制，并纳入四种基于新闻文本构建的不确定性冲击指标——经济政策不确定性、地缘政治风险、美国股市波动性和美国货币政策不确定性。该框架提升了结构可解释性，缓解了维度灾难问题，并实施了基于实证指导的正则化方法。利用G7国家数据，我们结合小波相干分析（时频动态特性）与非线性局部投影法（状态依赖脉冲响应），研究了不确定性冲击对五个核心变量（失业率、实际广义有效汇率、短期利率、石油价格和消费者价格指数通胀）的溢出效应。在12个月和24个月预测期内的样本外结果显示，SZBVARx模型在包括经典向量自回归模型和主流机器学习模型在内的14个基准模型中表现最优，这一结论通过Murphy差异图、多元Diebold-Mariano检验和Giacomini-White可预测性检验得到验证。基于可信贝叶斯方法的预测区间为情景分析和风险管理提供了稳健的不确定性量化工具。所提出的SZBVARx模型为G7政策制定者在普遍不确定性环境下进行现代宏观经济预测提供了透明且校准良好的分析工具。