This paper investigates whether structural econometric models can rival machine learning in forecasting energy--macro dynamics while retaining causal interpretability. Using monthly data from 1999 to 2025, we develop a unified framework that integrates Time-Varying Parameter Structural VARs (TVP-SVAR) with advanced dependence structures, including DCC-GARCH, t-copulas, and mixed Clayton--Frank--Gumbel copulas. These models are empirically evaluated against leading machine learning techniques Gaussian Process Regression (GPR), Artificial Neural Networks, Random Forests, and Support Vector Regression across seven macro-financial and energy variables, with Brent crude oil as the central asset. The findings reveal three major insights. First, TVP-SVAR consistently outperforms standard VAR models, confirming structural instability in energy transmission channels. Second, copula-based extensions capture non-linear and tail dependence more effectively than symmetric DCC models, particularly during periods of macroeconomic stress. Third, despite their methodological differences, copula-enhanced econometric models and GPR achieve statistically equivalent predictive accuracy (t-test p = 0.8444). However, only the econometric approach provides interpretable impulse responses, regime shifts, and tail-risk diagnostics. We conclude that machine learning can replicate predictive performance but cannot substitute the explanatory power of structural econometrics. This synthesis offers a pathway where AI accuracy and economic interpretability jointly inform energy policy and risk management.

翻译：本文探讨了结构计量经济学模型在预测能源-宏观经济动态时，能否在保持因果可解释性的同时与机器学习方法相竞争。利用1999年至2025年的月度数据，我们构建了一个统一框架，将时变参数结构向量自回归模型（TVP-SVAR）与先进的相依结构相结合，包括DCC-GARCH、t-copula以及混合Clayton-Frank-Gumbel copula。这些模型以布伦特原油为核心资产，在七个宏观金融与能源变量上，与主流机器学习技术——高斯过程回归（GPR）、人工神经网络、随机森林和支持向量回归——进行了实证比较评估。研究结果揭示了三个主要发现。首先，TVP-SVAR模型持续优于标准VAR模型，证实了能源传导渠道存在结构不稳定性。其次，基于copula的扩展模型比对称的DCC模型更有效地捕捉了非线性和尾部相依性，尤其是在宏观经济压力时期。第三，尽管方法学存在差异，copula增强的计量经济学模型与GPR达到了统计上等效的预测精度（t检验p值 = 0.8444）。然而，只有计量经济学方法能够提供可解释的脉冲响应、机制转换和尾部风险诊断。我们得出结论：机器学习可以复制预测性能，但无法替代结构计量经济学的解释力。这一综合框架为同时利用AI的预测精度与经济模型的可解释性来指导能源政策与风险管理提供了一条可行路径。