margins, dydx(experience) at(union=(0 1)) Use asdoc to send results directly to Word:
merge 1:1 id year using another_panel.dta 1:1 because each combination is unique. Learning Stata panel data commands is easy, but avoiding mistakes separates novices from experts. Pitfall 1: Forgetting to xtset Without xtset , commands like L.wage produce nonsense. Solution: Always xtset immediately after loading data. Pitfall 2: Ignoring Missing Data Patterns xtdescribe, patterns Shows which periods are missing for which panels. If missingness correlates with outcomes, you have attrition bias. Pitfall 3: Overlooking Time Fixed Effects Not including year dummies can make your FE model pick up economy-wide trends and claim them as treatment effects. Solution: Always include i.year or use xtreg, fe with time dummies. Pitfall 4: Using FE with Low Within Variation If experience barely changes for any worker, FE estimates will be imprecise. Check within variation via xtsum . Pitfall 5: Misinterpreting Hausman Test The Hausman test assumes homoskedasticity. Use hausman fe re, sigmamore for robust version. Part 8: Reporting Stata Panel Data Results Creating Regression Tables Using estout or outreg2 : stata panel data
Use reshape long to convert to :
Use pooled OLS as a baseline, but rarely as a final model. Fixed effects removes all time-invariant unobserved heterogeneity by de-meaning the data. It answers: Within a worker, how does changing union status affect wage? margins, dydx(experience) at(union=(0 1)) Use asdoc to send
regress wage educ experience union i.year : Simple, high efficiency if no unobserved heterogeneity. Cons : Biased if unobserved factors (e.g., ability) correlate with regressors. Solution: Always xtset immediately after loading data
use union_panel.dta xtset id year xtsum wage union experience