Before you can run a single regression, your data structure must be flawless. The "exclusive" secret to a clean workflow is mastering the xtset command and its validation counterparts. Beyond the Basics of xtset Most users know xtset id time . However, the pros use: xtset id time, delta(1) Use code with caution.
The solution is the or System GMM , specifically via the xtabond2 command (available via SSC). Why xtabond2 ? Unlike the built-in xtabond , xtabond2 allows for: Hansen J-tests for overidentifying restrictions. Arellano-Bond tests for autocorrelation. stata panel data exclusive
This produces , which are robust to all three issues, ensuring your p-values are actually reliable in complex datasets. Summary Checklist for your Stata Panel Project Set & Validate: xtset followed by xtdescribe . Decompose: Use xtsum to check for within-group variation. Test: Run a Hausman test (with robust options if needed). Adjust: Use L. and D. operators for lags and differences. Protect: Use vce(cluster id) or xtscc for inference. Before you can run a single regression, your
The standard Hausman test often fails when you have heteroskedasticity. In these cases, use the Wooldridge test or the sigmamore option to ensure your model selection is robust against non-constant variance. 3. Handling Dynamic Panels: The GMM Advantage However, the pros use: xtset id time, delta(1)
Variation over time for a single entity. If your "Within" variation is near zero, a Fixed Effects model will likely fail to produce significant results. 5. Modern Robustness: Driscoll-Kraay Standard Errors