Stata Panel Data Exclusive !!better!! ✦ (EXTENDED)

This is more efficient but assumes the individual effects are completely independent of the regressors. It allows for the inclusion of variables that don't change over time (like gender or race). 4. The Deciding Factor: The Hausman Test To decide between FE and RE, Stata users rely on the Hausman Test Run the FE model and type estimates store fixed Run the RE model and type estimates store random hausman fixed random significant p-value

), standard FE and RE estimators become severely biased due to . This requires dynamic panel data techniques. Arellano-Bond Difference GMM

If your variables are highly persistent, lagged levels make poor instruments for differenced variables. System GMM builds an augmented system by running equations in both differences and levels simultaneously. stata panel data exclusive

Ignores panel structure – use only as reference.

Estimate RE and store results xtreg y x1 x2, re estimates store re This is more efficient but assumes the individual

When using GMM, you must report two diagnostic statistics to validate your instrument strategy: Look at the p-value. It must be insignificant (

* 1. Run and store Fixed Effects xtreg y x1 x2 x3, fe estimates store fe_model * 2. Run and store Random Effects xtreg y x1 x2 x3, re estimates store re_model * 3. Run the Hausman test hausman fe_model re_model Use code with caution. If the p-value is small ( ), reject the null hypothesis. Use Fixed Effects . 3. High-Dimensional Fixed Effects (HDFE) The Deciding Factor: The Hausman Test To decide

xtunitroot llc y // Levin-Lin-Chu xtunitroot ips y // Im-Pesaran-Shin xtunitroot fisher y // Fisher-type

Before executing a single regression, Stata must understand the dimensional structure of your dataset. This requires defining the entity (cross-section) variable and the time variable. Data Cleaning and Formatting

Including time fixed effects controls for common shocks.

reg y x1 x2 i.id i.year