Always append vce(cluster id) if your diagnostic tests detect heteroskedasticity or serial correlation.

The following list of follow-up topics can help you tailor this panel data analysis workflow to your specific research goals or dataset requirements.

Stata also offers other flavors of linear panel models:

Measures how much the average values differ across units.

When you run xtreg ..., fe , look at the bottom of the output window for the F-test line: F test that all u_i=0 . -value is significant (

Real-world panel data rarely satisfies ideal assumptions. Standard errors can be distorted by heteroskedasticity, autocorrelation, or cross-sectional dependence. Heteroskedasticity and Autocorrelation

) , reject Pooled OLS in favor of Fixed Effects. Individual heterogeneity exists. Step 2: Fixed Effects vs. Random Effects (The Hausman Test) The Hausman test evaluates whether the individual errors ( ) are correlated with the regressors. The null hypothesis ( H0cap H sub 0

Run xtsum and xtline to understand your variations and trends. Estimate: Run your xtreg, fe and xtreg, re models.

When your model includes a lagged dependent variable as an explanatory variable ( yt−1y sub t minus 1 end-sub predicting

Stata will then respond with a summary of the panel structure:

regress wage experience union i.year, vce(cluster id)

Choosing between Pooled OLS, Fixed Effects, and Random Effects requires rigorous statistical testing. Fixed Effects vs. Pooled OLS (F-Test)

xtreg income education experience, re estimates store re_model Use code with caution. Run the Hausman test: hausman fe_model re_model Use code with caution.

FD is FE’s cousin, but in Stata, reg d.y d.x (manual first-differencing) gives different standard errors than xtreg, fd due to how Stata handles time gaps. For T=2, FD=FE. For T>2, FD is less efficient if errors are serially uncorrelated. But if errors follow a random walk, FD beats FE. Most Stata users never check.

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