: Treats observations as independent, ignoring the panel structure. Use only if you believe there are no entity-specific effects. reg y x1 x2 Use code with caution. Copied to clipboard
xtreg wage hours tenure age i.year, re
Why does this matter? Because panel data allows you to control for unobserved heterogeneity—the "invisible" variables that differ across entities but remain constant over time. For example, when studying the impact of education policy on test scores, panel data can control for inherent differences in school quality or regional culture that you cannot measure directly.
xtserial y x1 x2 x3
summarize gdp fdi trade gcf xtsum gdp fdi trade gcf // between and within variation
Random effects assumes the unobserved panel effects are uncorrelated with your regressors. This is a strong assumption but allows inclusion of time-invariant variables.
Use pooled OLS as a baseline, but rarely as a final model. stata panel data
No serial correlation (p>0.05).
collapse (mean) wage experience, by(id)
Alternatively, using areg or reghdfe (for high-dimensional FE): : Treats observations as independent, ignoring the panel
xtsum hours wage
If your dataset tracks countries over several years, and your country ID variable is country_id and your year variable is year , you would type: xtset country_id year
: Use xtdescribe to check if your panel is balanced (all entities observed for all years) or unbalanced. 2. Standard Panel Models Copied to clipboard xtreg wage hours tenure age i