Stata has a range of estimation commands for panel data. Here are some of the most commonly used:
The command handles fixed-effects transformations (forward orthogonal deviations) and produces robust standard errors clustered by panel ID.
xtreg ln_wage age tenure, fe
* System GMM execution using xtabond2 xtabond2 investment L.investment capital market_value, /// gmm(L.investment capital, lag(2 4)) iv(market_value) noleveleq twostep robust Use code with caution. Non-Stationary Panels: Unit Root Tests For long panels ( stata panel data exclusive
To solve this endogeneity issue in short, wide panels (small
Every GMM model requires validation through two diagnostic tests:
melogit y x1 x2 || id: x1, covariance(unstructured) Stata has a range of estimation commands for panel data
How much the variable changes from person to person. If a variable like education has zero within variation, it means individuals in your sample did not change their schooling level during the study window.
This produces , which are robust to all three issues, ensuring your p-values are actually reliable in complex datasets. Summary Checklist for your Stata Panel Project Set & Validate: xtset followed by xtdescribe . Decompose: Use xtsum to check for within-group variation. Test: Run a Hausman test (with robust options if needed). Adjust: Use L. and D. operators for lags and differences. Protect: Use vce(cluster id) or xtscc for inference.
) as an explanatory variable, standard FE and RE estimators suffer from dynamic panel bias (Nickell bias). Non-Stationary Panels: Unit Root Tests For long panels
Step 3: If endogenous xtdpdgmm y L.y x1, gmmstyle(y, lag(2 3)) ivstyle(x1) collapse
Before diving into estimation, Stata offers specialized tools for understanding your panel data structure:
xtdescribe tab panel_id, sort
xtreg y x1 x2, fe estimates a fixed effects model of y on x1 and x2 .