panelandmeid

Panel data, or longitudinal data, are datasets that follow the same units over multiple time periods. Each unit—such as a person, firm, or country—is observed repeatedly, creating a structure with both cross-sectional and time dimensions. In Estonian statistical usage the term panelandmeid is used for panel data.

Panel data enable control for unobserved, time-invariant characteristics and allow researchers to study dynamics by exploiting within-unit variation. They typically improve estimation efficiency and help identify causal effects under appropriate assumptions, particularly when there is heterogeneity across units.

Data structures can be balanced, where every unit has observations for all periods, or unbalanced, where some periods are missing because units enter or exit the sample.

Common analytical approaches include fixed-effects models, which remove time-invariant confounders by demeaning, and random-effects models, which assume unit effects are uncorrelated with other regressors. Dynamic panel models address lagged dependent variables, often using generalized method of moments (GMM) estimators to handle endogeneity and persistence.

Practical issues include handling missing data, attrition, non-stationarity, and measurement error. The key limitations are potential bias if assumptions fail, and the requirement for sufficiently long panels to identify effects.

Panel data are widely used in economics, finance, public health, and social sciences, derived from surveys, administrative records, and firm-level databases.