-test and LM test indicate that a panel model is needed, you must choose between FE and RE using the Hausman test.
In macro-panels (e.g., datasets covering multiple countries over many years), shocks like global financial crises can affect all entities simultaneously. This creates cross-sectional dependence. You can test for this using Pesaran’s CD test: xtcsd, pesaran abs Use code with caution.
xtreg income education experience, re estimates store re_model Use code with caution. Run the Hausman test: hausman fe_model re_model Use code with caution.
There are three foundational linear models used to estimate panel data: Pooled OLS, Fixed Effects, and Random Effects. A. Pooled OLS (Ordinary Least Squares) stata panel data
xtline ln_wage if idcode <= 20, overlay legend(off) title("Individual Wage Trajectories")
The xtsum command decomposes the total variance into between and within components. xtsum income leverage gdp Use code with caution.
When linear static models are insufficient, Stata offers specialized tools for advanced scenarios. Time Fixed Effects -test and LM test indicate that a panel
Use esttab or outreg2 to produce publication-ready tables.
Panel data, also known as longitudinal data, tracks the same cross-sectional units (such as individuals, firms, or countries) over multiple time periods. Analyzing panel data in Stata allows researchers to control for unobserved individual heterogeneity and model dynamic relationships.
Panel data, also known as longitudinal data, tracks the same cross-sectional units (such as individuals, firms, states, or countries) over multiple time periods. By combining both space and time dimensions, panel data allows researchers to control for unobserved individual heterogeneity and analyze dynamic changes. You can test for this using Pesaran’s CD
) is included as a predictor, standard FE models yield biased results (Nickell bias). The Arellano-Bond and Blundell-Bond estimators solve this via Generalized Method of Moments (GMM). : xtabond y x1 x2, gmm(y) iv(x1 x2) Use code with caution.
xtreg y x1 x2 x3, fe
xtreg ln_wage hours age tenure, fe
eststo clear eststo: reg ln_wage hours age tenure, vce(cluster idcode) eststo: xtreg ln_wage hours age tenure, fe eststo: xtreg ln_wage hours age tenure, re esttab est1 est2 est3, se star(* 0.10 ** 0.05 *** 0.01) /// mtitles("Pooled OLS" "Fixed Effects" "Random Effects") /// addnotes("Standard errors clustered at individual level")