Explainable Machine Learning for Macroeconomic and Financial Nowcasting: A Decision-Grade Framework for Business and Policy
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宏观与金融即时预测中的可解释机器学习：面向商业与政策决策的框架
- 研究问题
- 在数据丰富、易受冲击的宏观即时预测中，如何同时考虑预测精度、解释稳定性和不确定性，使季度 GDP 预测支持实际决策？
- 方法与思路
- 作者综述并比较计量基准与多类机器学习方法，讨论解释工具和区块自助法，并组织数据版本管理、时间验证及可靠性审查流程。
- 材料陈述的贡献
- 摘要提出面向决策的即时预测参考流程，将解释性和不确定性量化纳入系统设计，并列出制度变化与解释稳定性等研究议题。
- 阅读关注点
- 应核对实时数据版本与混频缺口的处理、样本外验证设计、区块自助法适用条件，以及解释稳定性如何被具体衡量。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要主要描述综述与流程，未给出统一实证比较或预测性能数值。
原文摘要
Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly GDP growth, but adoption in high-stakes settings requires that predictive accuracy be matched by interpretability and robust uncertainty quantification. This article reviews recent developments in macroeconomic nowcasting and compares econometric benchmarks with ML approaches in data-rich and shock-prone environments, emphasizing the use of nowcasts as decision inputs rather than as mere error-minimization exercises. The discussion is organized along three axes. First, we contrast penalized regressions, dimension-reduction techniques, tree ensembles, and neural networks with autoregressive models, Dynamic Factor Models, and Random Walks, emphasizing how each family handles small samples, collinearity, mixed frequencies, and regime shifts. Second, we examine explainability tools (intrinsic measures and model-agnostic XAI methods), focusing on temporal stability, sign coherence, and their ability to sustain credible economic narratives and nowcast revisions. Third, we analyze non-parametric uncertainty quantification via block bootstrapping for predictive intervals and confidence bands on feature importance under serial dependence and ragged edge. We translate these elements into a reference workflow for "decision-grade" nowcasting systems, including vintage management, time-aware validation, and automated reliability audits, and we outline a research agenda on regime-dependent model comparison, bootstrap design for latent components, and temporal stability of explanations. Explainable ML and uncertainty quantification emerge as structural components of a responsible forecasting pipeline, not optional refinements.