Description

This Master Prompt focuses on diagnosing whether the statistical assumptions underlying a fitted panel-data model are sufficiently supported for valid estimation and inference. It guides researchers in constructing a systematic diagnostic workflow that examines residual behavior, heteroskedasticity, serial correlation, cross-sectional dependence, covariance structure, sphericity, parameter instability, outliers, distributional departures, common shocks, latent-factor dependence, long-memory behavior, and other forms of misspecification. The objective is not merely to accumulate test statistics, but to determine which assumptions matter for the chosen estimator, how violations alter coefficient consistency or standard-error validity, and what corrective or robust inferential procedure is appropriate.