Stata | Panel Data

* Test for cross-sectional dependence xtcsd, pesaran abs * Estimate using Panel-Corrected Standard Errors (PCSE) xtpcse y x1 x2 x3 Use code with caution. Summary Workflow Cheat Sheet Stata Command xtset id time Defines entity and time dimensions Descriptives xtsum varlist Splits variance into within/between components Static Panel xtreg y x, fe / xtreg y x, re Runs fixed or random effects regressions LM Test xttest0 Compares Pooled OLS vs. Random Effects Hausman Test hausman fe_model re_model Compares Fixed Effects vs. Random Effects Robustness vce(cluster id) Controls for heteroskedasticity and autocorrelation Dynamic Panel xtabond / xtdpdsys Estimates models with lagged dependent variables

For more sophisticated research questions, Stata offers a wide array of advanced models.

Panel data consists of repeated observations of the same entities (e.g., individuals, firms, countries) over multiple time periods. stata panel data

Pooled OLS regress gdp fdi trade gcf eststo pooled

To fill in gaps with missing values (use cautiously): * Test for cross-sectional dependence xtcsd, pesaran abs

xtabond income education, gmm(income) iv(education) noleveleq Use code with caution. Summary of Essential Stata Panel Commands reshape long Converts wide layout to panel-ready long layout xtset id time Declares the entity and time variables to Stata xtsum

Stata's xt command family provides a comprehensive and powerful framework for panel data analysis. From the foundational xtset to advanced GMM methods like xtabond2 , Stata equips researchers with the tools needed to rigorously model complex longitudinal data. By mastering these commands and following established best practices, you can confidently explore dynamic relationships, control for unobserved heterogeneity, and produce credible, publication-ready results. Summary of Essential Stata Panel Commands reshape long

xtset panelvar timevar