Stata 17+ introduced two exclusive commands for causal inference with panel data:
melogit y x1 x2 || id: , or
This article is current as of Stata 19 (April 2025) and includes features from StataNow™ updates.
refers to datasets where the same entities (like individuals, firms, or countries) are observed over multiple time periods. To work with this data exclusively, you must define the panel structure so Stata can apply specialized xt (cross-sectional time-series) commands. 1. Setting Up the Panel stata panel data exclusive
Step 3: If endogenous xtdpdgmm y L.y x1, gmmstyle(y, lag(2 3)) ivstyle(x1) collapse
Once your data is set up, you can use various commands to describe and visualize your panel data:
Errors within an entity are often correlated over time. Wooldridge’s test for serial correlation in linear panel-data models is the standard tool, implemented via xtserial . xtserial investment capital market_value Use code with caution. Stata 17+ introduced two exclusive commands for causal
The "collapse" suboption to prevent "instrument proliferation"—a common pitfall that weakens the validity of your results. 4. Advanced Visualization for Panel Data
xtreg y x1 x2, re
Here is a guide on how to create, manage, and interpret exclusive dummy variables in Stata panel data. implemented via xtserial .
Always run xtdescribe immediately after setting your panel. This gives you a visual representation of your panel's "balance"—showing you exactly where the gaps in your data reside. 2. Dealing with Endogeneity: The Hausman Test & Beyond
If your variables are highly persistent, lagged levels make poor instruments for differenced variables. System GMM builds an augmented system by running equations in both differences and levels simultaneously.