Video Intelligence
For long panels (large T):
In panel data, the error term of one period may be correlated with the next.
In Fixed Effects models, you can test for groupwise heteroskedasticity using a modified Wald test (requires the user-written package xttest3 ).
Treats the data as one big cross-section, ignoring the panel structure.
Every beginner starts with xtset id year and then blissfully runs reg y x1 x2 i.year . The problem? Ignoring within-unit correlation. Stata makes RE easy ( xtreg y x1 x2, re ), but the selling point— xttest0 (Breusch-Pagan)—only tells you if there’s panel structure, not which estimator is consistent. : RE assumes no correlation between unit effects and regressors. In social sciences, that’s heroic. So most move to FE.
| Feature | Pooled OLS | Fixed Effects (FE) | Random Effects (RE) | | :--- | :--- | :--- | :--- | | | reg y x | xtreg y x, fe | xtreg y x, re | | Assumption | No individual effects | $\alpha_i$ correlated with $x$ | $\alpha_i$ NOT correlated with $x$ | | Time-Invariant Vars? | Yes | No (Dropped) | Yes | | Efficiency | N/A | Low | High | | Best For | Preliminary analysis | Causal inference (observational) | Efficiency / Random sampling |
xtdescribe // Summary of panel structure: balanced? gaps? xtsum // Summary statistics within and between panels xttab indvar // Tabulation by panel ID xtline yvar // Line plots for each panel (use as check for outliers)
Without xtset , panel data commands like xtreg , xtsum , xtline , or xtunitroot will not work. After running xtset , Stata remembers the panel structure for the entire session.
The between‑effects estimator regresses the unit means of the dependent variable on the unit means of the regressors:
xtsum hours work_age grade
A common error: two rows for the same idcode and year . This breaks panel structure.
Limited set of generative operations.
Agent with full control over the timeline, allowing human-like video editing without requiring any generation.
For long panels (large T):
In panel data, the error term of one period may be correlated with the next.
In Fixed Effects models, you can test for groupwise heteroskedasticity using a modified Wald test (requires the user-written package xttest3 ). stata panel data
Treats the data as one big cross-section, ignoring the panel structure.
Every beginner starts with xtset id year and then blissfully runs reg y x1 x2 i.year . The problem? Ignoring within-unit correlation. Stata makes RE easy ( xtreg y x1 x2, re ), but the selling point— xttest0 (Breusch-Pagan)—only tells you if there’s panel structure, not which estimator is consistent. : RE assumes no correlation between unit effects and regressors. In social sciences, that’s heroic. So most move to FE. For long panels (large T): In panel data,
| Feature | Pooled OLS | Fixed Effects (FE) | Random Effects (RE) | | :--- | :--- | :--- | :--- | | | reg y x | xtreg y x, fe | xtreg y x, re | | Assumption | No individual effects | $\alpha_i$ correlated with $x$ | $\alpha_i$ NOT correlated with $x$ | | Time-Invariant Vars? | Yes | No (Dropped) | Yes | | Efficiency | N/A | Low | High | | Best For | Preliminary analysis | Causal inference (observational) | Efficiency / Random sampling |
xtdescribe // Summary of panel structure: balanced? gaps? xtsum // Summary statistics within and between panels xttab indvar // Tabulation by panel ID xtline yvar // Line plots for each panel (use as check for outliers) Every beginner starts with xtset id year and
Without xtset , panel data commands like xtreg , xtsum , xtline , or xtunitroot will not work. After running xtset , Stata remembers the panel structure for the entire session.
The between‑effects estimator regresses the unit means of the dependent variable on the unit means of the regressors:
xtsum hours work_age grade
A common error: two rows for the same idcode and year . This breaks panel structure.
