Stata Panel Data «2026 Release»

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.

xtdpdgmm wage L.wage experience union, gmm(L.wage, lag(2 4)) iv(experience union) : GMM is powerful but complex. Check for overidentifying restrictions with Hansen test after estimation. 4. Fixed Effects with Individual Slopes If effects of time-varying variables differ across panels: stata panel data

xtserial wage union experience No serial correlation (p>0.05). regress wage educ experience union i

reshape long wage exp, i(id) j(year) gen wage_lag1 = L.wage // previous period gen wage_lead1 = F.wage // next period gen wage_diff = D.wage // change from t-1 to t These require xtset first. Collapsing Panels – When to Aggregate Sometimes you need panel means: reshape long wage exp, i(id) j(year) gen wage_lag1 = L

xtline wage, overlay

collapse (mean) wage experience, by(id) Or keep only first observation per panel:

bysort id (year): keep if _n == 1 Merging two panel datasets requires both dimensions: