For categorical or binary variables, xttab reveals how frequently entities transition between states over time. xttab unemployed Use code with caution.
* Syntax: xtset panelvar timevar [, options] xtset firm_id year Use code with caution.
Note: The standard hausman command assumes homoskedasticity. If you are using robust or clustered standard errors, use the user-written xtoverid or vce(robust) combined with a Mundlak approach. 4. Diagnostic Testing for Panel Data
Choosing the correct estimator determines whether your coefficients represent causal relationships or mere correlations. stata panel data exclusive
* setup xtset id year
Panel data is a type of data that combines cross-sectional and time series elements. It consists of observations on multiple individuals, firms, or countries at multiple points in time. This data structure allows researchers to examine changes over time, as well as differences across individuals or groups. Panel data is widely used in econometrics, finance, sociology, and other fields.
Before diving into models, you need to understand your panel's structure and patterns. For categorical or binary variables, xttab reveals how
The Fixed Effects model controls for all time-invariant, unobserved characteristics of your subjects (e.g., a person's genetics or a country's cultural history). It acts as if you added a dummy variable for every single cross-sectional unit.
xtreg y x1 x2, fe estimates store fixed xtreg y x1 x2, re estimates store random hausman fixed random Use code with caution. Reject Null → Use Fixed Effects . p > 0.05: Fail to reject → Use Random Effects . 4. Advanced Panel Techniques: Beyond Basics A. Handling Endogeneity: Instrumental Variables (FE-IV) If predictors are endogenous, use FE-IV. xtivreg2 y x1 (x2 = instrument), fe robust Use code with caution. B. Dynamic Panel Data (GMM) When the dependent variable depends on its past values ( yit−1y sub i t minus 1 end-sub
), Stata automatically removes time-invariant variables to avoid perfect collinearity Note: The standard hausman command assumes homoskedasticity
), standard FE models face . The lagged variable is mechanically correlated with the error term, causing severe endogeneity.
When heteroskedasticity or autocorrelation is present, standard errors must be adjusted. In panel data, you should almost always cluster your standard errors at the entity level. This allows for arbitrary correlation and heteroskedasticity within an entity while assuming independence between entities.
xtpcse leverage size profitability tangibility, correlation(ar1) Use code with caution. 5. Non-Stationary Panels: Unit Root Tests and Cointegration
reg y x1 x2 i.year, robust