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

In three linesAC-GATE, a neural model with adaptive gating, discovers how different entities (countries) respond to historical signals across varying time horizons in panel time series. The framework separates predictive calibration from lag discovery, validated on synthetic data with known ground-truth lags and two real country-level panels.

Summary generated by Claude — human-verified