Stata Panel Data Today
: Assumes entity-specific effects are uncorrelated with your independent variables. This allows you to include variables that don't change over time (like gender or race). xtreg y x1 x2, re Use code with caution. Copied to clipboard 3. Model Selection and Diagnostics
In the world of econometrics and data science, not all data is created equal. While cross-sectional data gives you a snapshot in time and time-series data tracks a single entity over time, (also known as longitudinal data) combines both dimensions. It follows multiple individuals, firms, countries, or other units across multiple time periods.
: Stata generally requires data in "long" format, where each row represents one observation per entity per time period.
), standard FE and RE models yield biased results (Nickell bias). In these scenarios, use Generalized Method of Moments (GMM) estimators like Arellano-Bond: xtabond income education age, gmm(income) iv(education age) Use code with caution. Summary Checklist for Stata Panel Analysis Key Stata Command Convert data to long format reshape long 2 Set up the panel identifiers xtset id time 3 Explore variation dimensions xtsum 4 Estimate baseline models xtreg ..., fe or xtreg ..., re 5 Decide between FE and RE hausman fe_model re_model 6 Correct for standard error bias Add vce(cluster id) stata panel data
summarize gdp fdi trade gcf xtsum gdp fdi trade gcf // between and within variation
For complex data issues, Stata provides specialized estimators.
Use reshape long to convert to :
xtset panelvar timevar
FE coefficient on union = 0.145 (p<0.01) → Union membership raises wage by 14.5% (semi-elasticity), holding experience and year constant.
Fixed Effects (preferred due to ability bias) xtreg wage union experience i.year, fe robust : Assumes entity-specific effects are uncorrelated with your
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.
If your data is in "wide" format (e.g., years as columns), use the command: reshape long [variable_stub], i(id) j(year) .
Variation over time within the same entities (ignoring differences between entities). Visualizing Panel Trajectories Copied to clipboard 3