If your diagnostics reveal heteroskedasticity or serial correlation, standard errors will be deflated, leading to false statistical significance. Fix this by adding vce(robust) or vce(cluster id) to your estimator:
Proper data management is crucial for panel data analysis. You will often need to reshape datasets, create lags, and generate new variables.
Serial correlation skews standard errors, making your coefficients look more statistically significant than they actually are. Use the Wooldridge test for autocorrelation (requires xtserial ). ssc install xtserial xtserial GDP inflation trade_openness Use code with caution.
xttab married
Stata’s xt family extends far beyond xtreg . Here are the most important advanced commands:
The Random Effects model assumes that the unobserved individual heterogeneity is completely uncorrelated with the explanatory variables. xtreg GDP inflation trade_openness, re Use code with caution.
and adjust your standard errors accordingly. stata panel data
The null hypothesis of the Hausman test is that the unit‑specific effects are uncorrelated with the regressors (i.e., RE is consistent and efficient). The alternative is that they are correlated (RE is inconsistent, FE is consistent).
The xtsum command decomposes the variance of your variables into , between (across units), and within (over time for a single unit) components: xtsum income education Use code with caution. Tabulating the Panel Structure
—where you track multiple entities (individuals, firms, countries) over several time periods. xttab married Stata’s xt family extends far beyond xtreg
Master Panel Data Analysis in Stata: A Comprehensive Guide Panel data, also known as longitudinal data, tracks the same cross-sectional units (individuals, firms, countries) over multiple time periods. Analyzing panel data allows researchers to control for unobserved individual characteristics and model dynamic changes.
The xt suite, documented in Stata's 698-page Longitudinal-Data/Panel-Data Reference Manual , provides a complete toolkit for this specialized analysis. The table below outlines the most essential xt commands, categorized by their primary use case.