To control for macro-level shocks that affect all entities simultaneously (e.g., a global recession or pandemic), include time fixed effects by adding time dummies: xtreg gdp investment unemployment i.year, fe Use code with caution. Dynamic Panel Data: Difference and System GMM If your model includes a lagged dependent variable ( Yt−1cap Y sub t minus 1 end-sub
gen lag_gdp = L.gdp
The FE model controls for unobserved, time-invariant characteristics by essentially looking at the variation each entity over time. It estimates a separate intercept for each entity, effectively removing the bias from omitted time-invariant variables. This is why variables that do not change over time (like race or gender) cannot have their coefficients estimated in an FE model; they are omitted because they are collinear with the entity-specific intercepts. stata panel data
xtdescribe * Shows the participation pattern of entities over time xtsum * Decomposes variance into "overall", "between" (across units), and "within" (over time) Use code with caution.
Choosing blindly between Pooled OLS, FE, and RE can invalidate your empirical findings. Stata provides specific post-estimation tests to guide your selection. To control for macro-level shocks that affect all
Pooled OLS ignores the panel structure and treats all observations as independent. It is rarely the optimal model because it ignores unobserved entity-specific characteristics.
: Each row is an observation for a specific entity at a specific time point. This is why variables that do not change
Before running any analysis, you must tell Stata which variable identifies the units and which identifies the time.
Want to include a lagged dependent variable? FE is inconsistent (Nickell bias). Enter Arellano-Bond ( xtabond ). Stata’s implementation is powerful but:
The primary command for this is . You tell Stata which variable identifies the panels (the cross-sectional units) and, optionally, which variable identifies the time periods. The basic syntax is: