追蹤資料分析 (Panel Data Analysis)

Overview

Panel data analysis exploits both cross-sectional and temporal variation to estimate causal effects while controlling for unobserved heterogeneity. Fixed effects eliminate time-invariant confounders through within-entity demeaning, while random effects assume unobserved heterogeneity is uncorrelated with regressors, yielding more efficient estimates when valid.

When to Use

• Data has repeated observations for the same entities (firms, individuals, countries) over time
• Unobserved time-invariant factors likely confound the relationship of interest
• Testing whether a policy or treatment effect varies across time periods
• Dynamic models where the lagged dependent variable is a regressor (use GMM)

When NOT to Use

• Pure cross-sectional data with no time dimension
• Interest is in estimating the effect of time-invariant variables (FE eliminates these)
• Panel is extremely short (T = 2) with many endogenous regressors
• Attrition is non-random and creates survivorship bias