grad-panel-data
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
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