) is included as a regressor, static estimators become inconsistent due to Nickell bias (
This will estimate a random-effects model of y on x1 and x2 .
Your panel variable must be a numeric integer. If your identifier is a string (e.g., country ISO codes or company names), convert it using encode . stata panel data exclusive
Mastering panel data in Stata requires understanding not just the commands, but the underlying assumptions of FE and RE models. By leveraging xtset , performing thorough diagnostics with xtsum , and choosing the correct model using hausman , you can produce robust, academically rigorous analysis. If you'd like, I can: Show you Explain the difference between xtivreg2 and xtabond2 Help you visualize interaction effects in panel models
Note: There is no incidental parameter problem solved for Fixed Effects Probit in standard Stata; avoid using unconditional fixed effects with probit and dummy variables. 5. Summary Cheat Sheet for Panel Analysts Key Consideration xtset id time Must be numeric identifiers. Data Description xtsum / xtdes Separates within and between variation. Primary Estimation xtreg y x, fe / xtreg y x, re Choose via the hausman command. Autocorrelation xtserial y x Crucial for long time-series dimensions ( Endogeneity Fix xtivreg / xtabond Uses instrumental variables or GMM. Robust Inference , vce(cluster id) Corrects for heteroskedasticity automatically. ) is included as a regressor, static estimators
If your diagnostics reveal heteroskedasticity, serial correlation, or mild cross-sectional dependence, adjust your standard errors by clustering at the entity level. xtreg y x1 x2 x3, fe vce(cluster company_id) Use code with caution. 4. Advanced Panel Estimators
) is larger than or equal to the cross-sectional dimension ( Mastering panel data in Stata requires understanding not
Standard OLS assumptions rarely hold in panel datasets. Ignoring diagnostics can lead to severely understated standard errors and spurious statistical significance. Heteroskedasticity