提出新模型发现各国对历史信号响应的差异滞后期。
Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series
- 用实体特征动态调节滞后权重分布，让有效滞后成为模型输出
- 在模拟数据中准确恢复已知滞后结构，在真实数据中生成合理滞后
- 适合做跨国经济面板分析的学者和政策研究者
国家层面的时间序列面板广泛用于实证分析。研究人员常需审计不同实体如何在不同时间跨度内响应历史信号。现有方法通常无法提供可直接审计的实体特异性滞后总结。本文将实体条件下的异质滞后发现建模为时间面板挖掘任务，提出AC-GATE：一种自适应条件编码器搭配尺度不变滞后门。该模型通过可观测的实体级代理变量，条件化历史观测的滞后权重分布，使有效滞后成为模型的结构性输出而非事后解释。评估采用分层审计协议，区分预测校准与滞后发现。使用具有已知真实滞后结构的合成面板测试机制恢复能力，另用两个真实世界国家层面面板进行外部审计与压力测试。结果表明，AC-GATE可在合成数据中恢复异质滞后结构，并在真实数据中生成非退化的、外部一致的有效滞后。
原文摘要 · Abstract (English)
Country-level temporal panels are widely used in empirical analysis. Researchers often need to audit how different entities respond to historical signals over different time horizons. Current approaches typically do not provide directly auditable entity-specific lag summaries. We formulate entity-conditioned heterogeneous lag discovery as a temporal panel mining task and propose AC-GATE, an Adaptive-Conditioning Encoder with a Scale-Invariant Lag Gate. It instantiates conditional Moderated Distributed Lag by using observable entity-level proxies to condition lag-weight distributions over historical observations, thereby making effective lags structural outputs of the model rather than post-hoc explanations. The evaluation is based on a layered audit protocol that separates predictive calibration from lag discovery. A synthetic panel with known ground-truth lags is used for mechanism recovery testing, and two real-world country-level panels are used for external audit and stress testing. The results show that AC-GATE can recover heterogeneous lag structure in synthetic data, and generates non-degenerate, externally structured effective lags in real data.
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