How Many Groups? Adaptive Selection in Panel Data under Model Uncertainty

Latent group structures are central to panel data econometrics, yet the true number of groups is rarely known. This paper systematically evaluates 16 estimators—spanning clustering heuristics, simulation-based techniques, exact inference, and spectral methods—under realistic conditions of model misspecification. Across diverse data-generating processes (including dynamic trends, weak factors, and spatial spillovers), the analysis reveals that while spectral estimators excel under strong factor structures, clustering and simulation-based approaches offer superior robustness to weak signals and non-standard dependencies. To address this methodological uncertainty, I propose a data-driven adaptive selection strategy that aggregates multi-criteria evidence to reliably identify the optimal group count. Finally, to facilitate empirical practice, I provide a unified R toolkit and demonstrate the adaptive selector’s value as a robust pre-estimation diagnostic tool by revisiting the macroeconomic democracy–income nexus.