Stability-Constrained Regime-Aware Forecasting for Heterogeneous Panel Time Series

Social science panel time series exhibit multicollinearity, latent unit heterogeneity, and regime-conditional sign-changing relationships. On V-Dem democratic development data, four of four selected covariates exhibit coefficient sign reversal across regimes, driving pooled additive predictors to cancel real signal - a diagnostic we establish before introducing any nonlinear model. We present a regime-aware additive forecasting architecture that recovers this signal under a verifiable contraction certificate. Our theoretical contribution is a rate-based contraction theorem decomposing the global Lipschitz constant of a soft-gated mixture predictor into per-regime, regime-disagreement, and dwell-time components, each empirically auditable on a trained model. On V-Dem democratic development data ($151$ countries, $1970-2025$), the architecture recovers regime-conditional signal that pooled additive models cancel (paired Diebold-Mariano $p=0.016$), with the recovery localized to the tails of the democracy index where a diagnostic shows largest covariate sign disagreement. The contraction certificate $L_{\text{eq,max}}<1$ holds in $5/5$ seeds across all $9$ audited configurations without explicit projection. A synthetic stress test with stronger regime contrasts shows the certificate is non-vacuous: spectral normalization on per-edge layers can fail to deliver contraction because the gating-driven term $L_{\phi,\text{eq}} \cdot M$ dominates, and a regime-disagreement penalty is required to recover the bound.