Proper data management is crucial for panel data analysis. You will often need to reshape datasets, create lags, and generate new variables.
If the assumptions of FE/RE are violated, advanced methods are required.
The Random Effects model explores both and between-unit variation. It assumes that the unobserved individual heterogeneity is completely uncorrelated with the explanatory variables. xtreg y x1 x2 x3, re Use code with caution. stata panel data
ssc install xtabond2 xtabond2 GDP l.GDP inflation trade_openness, gmm(l.GDP) iv(inflation trade_openness) nolevel small Use code with caution. Non-Linear Panels (Logit and Probit)
Standard linear regression assumptions rarely hold in panel data. You must test for heteroskedasticity, serial correlation, and cross-sectional dependence. Testing for Heteroskedasticity Proper data management is crucial for panel data analysis
After declaring the panel, Stata will remember this structure. If you've previously set the data for time-series using tsset , xtset will recognize it.
Choosing the right model depends on your assumptions about "unobserved heterogeneity"—factors unique to individuals that don't change over time (like innate ability or geography). The Random Effects model explores both and between-unit
) rejects the null hypothesis, meaning significant panel effects exist. You should choose Random Effects over Pooled OLS. Fixed Effects vs. Random Effects (Hausman Test)
: If your variables are correlated with the error term, use Instrumental Variables ( xtivreg ).