Model Selection Strategy in Modern Panel Data Econometrics
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,