-values. Always append clustered robust standard errors to your regressions: xtreg income education age, fe vce(cluster id) Use code with caution. Testing for Autocorrelation
Dynamic panel models include lagged dependent variables as regressors ( y_i,t-1 ). Using standard FE on such a model leads to the "Nickell bias". To solve this, researchers use Generalized Method of Moments (GMM) estimators.
In Fixed Effects models, you can test for groupwise heteroskedasticity using a modified Wald test (requires the user-written package xttest3 ).
Tests xttest0 hausman fe re
After estimating a model, researchers must check for potential violations of assumptions. The predict command generates residuals and fitted values. Key diagnostics include: stata panel data
The standard summarize command pools all observations together. To get a breakdown of the variation within units over time versus between different units, use xtsum : xtsum income Use code with caution. The output provides three distinct standard deviations: Variation across the entire pooled dataset.
This command calculates the summary statistics for the dependent variable depvar and the independent variable indepvar .
margins, dydx(experience) at(union=(0 1))
(Reject the Null): The random effects assumptions are violated. The coefficients are systematically different, indicating bias. If -values
: If your data is in "wide" format (one row per entity with multiple columns for different years), use the reshape long Declaration : You must tell Stata the data is a panel using the xtset panelvar timevar xtset country year 2. Descriptive Reporting
Panel data estimation typically revolves around three core frameworks: Pooled OLS, Fixed Effects, and Random Effects. Pooled OLS Regression
When your model includes a lagged dependent variable (e.g., using last year's income to predict this year's income), standard FE and RE models yield biased estimates due to Nickell bias.
For a dataset of companies id observed across years year , the command is: Using standard FE on such a model leads
is the gold-standard software for panel data analysis. Its intuitive syntax, powerful built-in commands, and robust error-handling make it the preferred choice for academic researchers, economists, and data analysts worldwide.
Create correlated variables gen fdi = 2 + 0.5 invnorm(uniform()) + 0.3 (country/10) + 0.02*(year-2000) gen trade = 40 + 8 invnorm(uniform()) + 0.5 (year-2000) gen gcf = 20 + 3 invnorm(uniform()) gen gdp = 7 + 0.15 fdi + 0.01 trade + 0.05 gcf + rnormal(0,0.3) + 0.1*(country/5)
: Each row is an entity, and time-varying variables are columns (e.g., gdp2010 , gdp2011 ).