Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series

TL;DR

This paper introduces AC-GATE, a neural framework for discovering entity-specific lag structures in panel time series, enabling more interpretable and accurate analysis of temporal responses.

Contribution

It proposes a novel lag discovery method that makes effective lags structural outputs, improving interpretability over existing approaches.

Findings

AC-GATE successfully recovers heterogeneous lag structures in synthetic data.

It generates meaningful, externally structured effective lags in real-world country panels.

The layered audit protocol effectively separates predictive calibration from lag discovery.

Abstract

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.