If experience barely changes for any worker, FE estimates will be imprecise. Check within variation via xtsum .
Or keep only first observation per panel:
: Use the xtset command to define the individual identifier ( ) and the time variable ( Command : xtset id_variable time_variable .
quietly xtreg y x1 x2, fe estimates store fixed quietly xtreg y x1 x2, re estimates store random hausman fixed random Use code with caution. Copied to clipboard : For long panels ( stata panel data
In datasets with many entities, especially macro panels, errors may be correlated across entities. You can test for this with the community-contributed xtcsd command. If present, you can use estimators like standard errors (with vce(dkraay) ) or models like the Dynamic Common Correlated Effects estimator (with community-contributed commands like xtdcce2 ).
The RE model ( xtreg, re ) assumes that the unobserved entity-specific effects are independent of the explanatory variables. It allows for between entity comparisons. xtreg ln_wage grade age, re Use code with caution. 4.4. Fixed vs. Random Effects: The Hausman Test To choose between FE and RE, perform the Hausman test. Random effects is preferred (efficient). Alternative ( HAcap H sub cap A ): Fixed effects is preferred (consistent).
Stata's xt commands make panel data analysis accessible and robust. By utilizing xtset , exploring with xtsum , and choosing the right estimator through xtreg , you can uncover the dynamic relationships within your data, effectively controlling for unobserved heterogeneity. If experience barely changes for any worker, FE
There are three foundational linear models used to estimate panel data: Pooled OLS, Fixed Effects, and Random Effects. A. Pooled OLS (Ordinary Least Squares)
By mastering these commands and understanding the underlying models, you can perform rigorous longitudinal analysis, ensuring your results account for both the static and dynamic components of your panel data [5.4]. If you'd like, I can provide: A specific using a sample dataset How to interpret the output of a xtreg, fe model How to handle missing data in panel sets
The key steps to remember are:
: The numeric variable identifying the cross-sectional unit. timevar : The numeric variable identifying the time period.
┌──────────────────────────────┐ │ Panel Data Analysis │ └──────────────┬───────────────┘ │ ┌───────────────────────┼───────────────────────┐ ▼ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Pooled OLS │ │ Fixed Effects │ │ Random Effects │ │ (regress) │ │ (xtreg, fe) │ │ (xtreg, re) │ └─────────────────┘ └─────────────────┘ └─────────────────┘ A. Pooled OLS ( regress )
No subsequent xt command works correctly without it. Declaring the panel structure also enables Stata’s time‑series operators ( L. , F. , D. ) to work safely across panels. quietly xtreg y x1 x2, fe estimates store