This post outlines a comprehensive framework for model selection, estimation, and validation in modern panel-data econometrics. Rather than treating panel-data analysis as a simple choice between fixed-effects and random-effects estimators, the framework views model selection as a sequential process driven by the statistical properties of the underlying data-generating process. The proposed approach integrates key diagnostic procedures, including tests for cross-sectional dependence, panel unit roots, structural breaks, slope heterogeneity, cointegration, and endogeneity, to identify the set of econometrically admissible estimators. The framework further examines the theoretical foundations, assumptions, advantages, and limitations of major panel-data estimators, including static panel models, instrumental-variable approaches, dynamic panel estimators based on the Generalized Method of Moments (GMM), long-run cointegration models such as Mean Group (MG), Pooled Mean Group (PMG), and Dynamic Fixed Effects (DFE), as well as second-generation estimators designed to accommodate cross-sectional dependence, including Common Correlated Effects (CCE), Dynamic Common Correlated Effects (DCCE), and Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) models. Particular attention is devoted to the role of sample dimensions, panel balance, and post-estimation diagnostics in determining estimator validity and robustness. The study proposes an integrated decision framework linking diagnostic outcomes to appropriate model choices and demonstrates how econometric specification, estimation, validation, and robustness analysis should be viewed as an iterative process. The resulting framework provides researchers with a systematic guide for selecting panel-data estimators that are theoretically consistent, statistically valid, and empirically robust across a wide range of economic applications.