Stata Panel: Data

Mastering requires practice and reference.

xtserial wage union experience

The Fixed Effects model explores the variation. It controls for all time-invariant, unobserved characteristics of the entities (e.g., a country's culture, an individual's genetic traits) by subtracting the group-level means from each variable. xtreg y x1 x2 x3, fe Use code with caution.

using do files. Because panel data often require complex reshaping, merging, and lag generation, a well‑commented script is essential for replication. stata panel data

estat abond

xtdescribe // Summary of panel structure: balanced? gaps? xtsum // Summary statistics within and between panels xttab indvar // Tabulation by panel ID xtline yvar // Line plots for each panel (use as check for outliers)

No subsequent xt command works correctly without it. Declaring the panel structure also enables Stata’s time‑series operators ( L. , F. , D. ) to work safely across panels. Mastering requires practice and reference

Stata’s panel data capabilities continue to expand. To go deeper:

(First-stage: tenure instrumented by age.)

Stata’s xt commands provide a complete, integrated environment for panel data analysis—from data declaration and exploration to advanced estimation and post‑estimation diagnostics. Whether you are estimating a simple fixed‑effects model or a complex system GMM specification, mastering these tools will enable you to control for unobserved heterogeneity, exploit both dimensions of your data, and produce estimates that are both precise and robust. xtreg y x1 x2 x3, fe Use code with caution

Raw panel data often arrives messy. Prepare it systematically.

xtunitroot llc wage, trend lags(2) // Levin-Lin-Chu xtunitroot ips wage, trend lags(2) // Im-Pesaran-Shin xtunitroot fisher wage, trend lags(2) // Fisher-type

Pooled Ordinary Least Squares (OLS) acts as if the panel structure does not exist, simply pooling all observations together.