It completely drops any variables that do not change over time (e.g., race, gender, or geographic location). Random Effects: Maximizing Efficiency
Choosing the correct estimator determines whether your coefficients represent causal relationships or mere correlations.
Do you suspect in your model?
Run xtdescribe immediately after setting your panel. This visualizes your data's patterns, showing whether your panel is strictly balanced (every unit observed at every time point) or unbalanced (missing periods for certain units). 2. Linear Frontiers: Fixed vs. Random Effects
The difference between a standard Stata user and an one is not just knowing xtreg —it is mastering high-dimensional FE, cross-sectional dependence, dynamic GMM, and non-linear multilevel models. It is understanding when to use reghdfe over xtreg , when to apply xtscc errors, and how to validate instruments in xtdpdgmm . stata panel data exclusive
xtabond2 allows for precise control over instrument proliferation, a common issue that weakens the validity of GMM results. Always check the Hansen J-test for instrument validity and the Arellano-Bond test for autocorrelation (AR(2)) outputted by this command. 4. Advanced Diagnostics: The "Must-Dos"
The community-contributed package reghdfe solves this problem by using an optimized algorithm to sweep out high-dimensional fixed effects. It completely drops any variables that do not
To choose between FE and RE objectively, run a Hausman specification test. The null hypothesis states that the RE estimator is efficient and consistent.