) rejects the null hypothesis, indicating that Fixed Effects is the appropriate model. 3. Vital Post-Estimation Diagnostics
Similarly, for count data (patents, accidents), skip xtnbreg and use menbreg (multilevel negative binomial):
help whatsnew18 // Look for "Panel data" section
command explicitly defines the panel structure (entity ID and time variable), allowing the software to automatically account for the data's nested nature and correctly calculate standard errors. Rich Documentation : Stata provides a comprehensive Longitudinal-Data/Panel-Data Reference Manual stata panel data exclusive
This will test whether the fixed-effects or random-effects model is more appropriate.
Panel data analysis is a powerful tool for studying economic and social phenomena over time. Stata offers an extensive range of tools and techniques for analyzing panel data, including descriptive statistics, regression analysis, and advanced techniques such as dynamic panel models and instrumental variables. By following the best practices outlined in this article and using the correct Stata commands, researchers can unlock the full potential of panel data analysis and gain valuable insights into the behavior of individuals and groups over time.
When the Hausman test rejects RE but you still need to estimate the coefficients of time-invariant variables, use the Mundlak (1978) approach. This method adds the panel-level means of time-varying covariates to a Random Effects model. ) rejects the null hypothesis, indicating that Fixed
xtset id year , where id is the individual identifier and year is the time variable.
If we drop status_1 (Private firms), we interpret coefficients relative to private firms.
The difference between a standard Stata user and an one is not just knowing xtreg —it is mastering high-dimensional FE, cross-sectional dependence, dynamic GMM, and non-linear multilevel models. It is understanding when to use reghdfe over xtreg , when to apply xtscc errors, and how to validate instruments in xtdpdgmm . By following the best practices outlined in this
Step 3: If endogenous xtdpdgmm y L.y x1, gmmstyle(y, lag(2 3)) ivstyle(x1) collapse
Models, using xtreg, re , assume that the unobserved panel-specific effects are uncorrelated with the regressors. They are more efficient than FE but rely on a stricter assumption. The choice between FE and RE is often guided by the Hausman test ( hausman after estimations).
: Used as a baseline for comparison but often ignored because it fails to account for the correlation within panels. Fixed Effects (FE)
This tells Stata that your data is panel data with individual ID ( id ) and year ( year ) as the time variable.
The Hausman test determines if the unobserved individual effects are correlated with your regressors. RE is consistent and efficient (no correlation). Alternative Hypothesis ( Hacap H sub a