Panel data (also called longitudinal or cross-sectional time-series data) combines both cross-sectional and temporal dimensions, observing multiple entities (individuals, firms, countries) repeatedly over time. This structure enables researchers to control for unobserved heterogeneity that remains constant over time, substantially reducing omitted variable bias compared to pure cross-sectional or time-series approaches. Panel methods are fundamental in econometrics, empirical research, and causal inference, with applications spanning labor economics, health policy, finance, and social sciences. A critical distinction in panel data analysis is understanding the source of variation: whether identification comes from changes within entities over time (within variation) or differences between entities (between variation), as different estimators exploit different dimensions of the data structure.