Stata Panel Data

If the outcome is binary (e.g., employed/unemployed), use panel logit. xtlogit employed grade age, fe Use code with caution. Summary of Essential Stata Panel Commands xtset id time Summary Stats xtsum Fixed Effects xtreg y x, fe Random Effects xtreg y x, re Hausman Test hausman Dynamic Model xtabond Conclusion

: Clean and import your dataset, then explicitly declare the panel structure using xtset . 2. Explore & Balance : Use xtdescribe and xtsum to understand gaps and variation, addressing issues like duplicates or missing time points. 3. Estimate Model : Start with benchmark xtreg, fe and xtreg, re models, performing the Hausman test to guide your initial choice. 4. Address Endogeneity : If concerns exist (e.g., reverse causality), advance to xtivreg or dynamic GMM estimators, carefully evaluating instrument validity. 5. Validate & Robustness : Perform diagnostic tests for serial correlation, use robust standard errors, and test the sensitivity of your results to model specification.

Stata uses the xtreg command to estimate linear panel data models. The three most common approaches are Pooled OLS, Fixed Effects, and Random Effects. 1. Pooled OLS

Robust FE xtscc gdp fdi trade gcf, fe lag(2) estimates table pooled fe re, b se stata panel data

, where each row represents a single entity at a single point in time.

Before running any analysis, you must tell Stata which variable identifies the entity (panel ID) and which identifies the time. use http://stata-press.com xtset idcode year Use code with caution. Copied to clipboard Why it matters: This enables Stata’s suite of commands and allows for the use of Time-Series Operators (lagged GNP) or (the first difference of unemployment). 2. The Big Two: Fixed vs. Random Effects

The RE model assumes that the entity-specific effects are uncorrelated with the explanatory variables. xtreg y x1 x2, re Use code with caution. If the outcome is binary (e

Panel data combines (many entities, one time) and time-series data (one entity, many times). Entities ( ): (e.g., households) Time Periods ( ): (e.g., years) Types of Panels

Significant p-value → RE is better than pooled OLS.

This model was like a personal filter. It ignored things that stayed the same for a person (like their birthplace or genetic traits) and only looked at how in education led to Estimate Model : Start with benchmark xtreg, fe

Transition probabilities (useful for categorical data like employment status) xttrans dependent_var

): Represents the individual units (e.g., 1,000 distinct people). Time dimension (