xtreg y x1 x2, fe // Fixed-effects (within) estimator xtreg y x1 x2, re // Random-effects estimator xtreg y x1 x2, be // Between-effects estimator xtreg y x1 x2, pa // Population-averaged estimator
This "exclusive" variance-covariance estimation ensures that your standard errors are robust to arbitrary serial correlation within the entity.
| Command | Purpose | |---------|---------| | xtdescribe | Displays the pattern of panel data, including gaps, time spans, and frequencies | | xtsum | Provides summary statistics that decompose variation into between-entity and within-entity components | | xttab | Tabulates variables across panel dimensions | | xtline | Plots time series for each panel unit, invaluable for visualizing trends and outliers | stata panel data exclusive
To resolve this, leverage the Difference and System Generalized Method of Moments (GMM), optimized in David Roodman’s xtabond2 package. Implementing System GMM
). If this is high, your entities differ significantly from one another. : Variance calculated over time within each entity ( xtreg y x1 x2, fe // Fixed-effects (within)
Panel data (or longitudinal data) tracks the same cross-sectional units—like individuals, firms, or countries—over multiple time periods. While standard OLS regressions fail to capture the complex dependency structures of these datasets, Stata offers an industry-leading suite of tools specifically built to handle them.
Once in long format, declare the dataset as a panel using xtset : xtset firm_id year Use code with caution. Understanding the Output If this is high, your entities differ significantly
The traditional hausman command fails if your model violates the assumption of homoscedasticity. To execute a cluster-robust Hausman test, you must use an auxiliary regression approach via the user-written command xtoverid (installable via ssc install xtoverid ).
If the test rejects the null hypothesis of homoskedasticity, you must adjust your standard errors. Autocorrelation (Serial Correlation)
While standard regressions ( regress ) work, Stata offers exclusive commands that correctly calculate degrees of freedom for panel data.
Panel data—tracking the same cross-sectional units over multiple time periods—is the gold standard for causal inference in observational research. By observing the same entities over time, you can control for unobserved time-invariant heterogeneity, effectively eliminating a massive source of omitted variable bias.