Fleshbot Fleshbot ClipHunter ClipHunter

Visualizing trends is crucial before modeling.

* Plot a single variable over time for all units (spaghetti plot)
xtline gdp

xtreg wage hours tenure age i.year, re



Stata ships with example datasets. For panel data, the most famous is nlswork.dta (National Longitudinal Survey of Young Women, 1968–1988).


webuse nlswork, clear



This dataset contains 28,534 observations on 4,711 individuals (ID variable: idcode) across up to 12 years.




xtreg wage hours tenure, fe robust



or


xtreg wage hours tenure, fe vce(cluster idcode)



Already achieved with i.year in the FE model.


Alternative to RE. Does not model the individual effect explicitly but accounts for the correlation structure within panels.


For interpretation, compute marginal effects:


margins, dydx(experience) at(union=(0 1))

Stata Panel Data Instant

Visualizing trends is crucial before modeling.

* Plot a single variable over time for all units (spaghetti plot)
xtline gdp

xtreg wage hours tenure age i.year, re



Stata ships with example datasets. For panel data, the most famous is nlswork.dta (National Longitudinal Survey of Young Women, 1968–1988). stata panel data


webuse nlswork, clear



This dataset contains 28,534 observations on 4,711 individuals (ID variable: idcode) across up to 12 years. Visualizing trends is crucial before modeling




xtreg wage hours tenure, fe robust



or


xtreg wage hours tenure, fe vce(cluster idcode)



Already achieved with i.year in the FE model. Stata ships with example datasets


Alternative to RE. Does not model the individual effect explicitly but accounts for the correlation structure within panels.


For interpretation, compute marginal effects:


margins, dydx(experience) at(union=(0 1))