The xtsum command decomposes the total variance into between and within components. xtsum income leverage gdp Use code with caution.
A (e.g., p < 0.05) rejects the null hypothesis, suggesting that FE is preferred; a high p‑value indicates that RE may be adequate. Because the test often produces a negative chi‑squared statistic when the variance‑covariance matrix is not positive definite, many researchers now supplement the Hausman test with the xtoverid command (available after RE estimation), which provides a robust version.
* Difference GMM (Arellano-Bond) xtabond y x1 x2, gmm(y) iv(x1 x2) Use code with caution. 6. Summary Cheat Sheet of Essential Stata Panel Commands xtset id time Defines the panel and time variables. xtsum varlist Summarizes variables across within/between dimensions. xtline var Generates time-series line plots for panel units. xtreg y x, fe Estimates a Fixed Effects model. xtreg y x, re Estimates a Random Effects model. hausman fe re Tests whether to use Fixed Effects vs. Random Effects. xtscc y x, fe Estimates FE with Driscoll-Kraay robust standard errors. xtabond y x Estimates dynamic panel models using Difference GMM. To help tailor any further modeling advice, let me know:
[ Pooled OLS ] | | Poolability Test / F-test v [ Fixed Effects (FE) ] ^ | | Hausman Test v [ Random Effects (RE) ] ^ | | Breusch-Pagan LM Test v [ Pooled OLS ] Step 1: Fixed Effects vs. Pooled OLS (F-test) When you run xtreg ..., fe , Stata automatically includes an
Stata offers three foundational models for panel regression: Pooled OLS, Fixed Effects, and Random Effects. Pooled OLS stata panel data
Stata will report whether the panel is (all units observed at all times) or unbalanced (missing time periods for some units). Stata's algorithms automatically accommodate unbalanced structures. Step 3: Visualizing the Data
What make up your independent variables (are they mostly time-invariant, like gender, or time-varying, like GDP)?
: Use xtdes to see how many years of data you have for each entity and if the panel is "balanced" (everyone has data for all years) .
This decomposition is fundamental: it tells you whether most of the variation in a variable is across units (e.g., race, gender) or within units over time (e.g., income, unemployment status). The xtsum command decomposes the total variance into
If your dependent variable is binary (0 or 1), you cannot use linear xtreg . Stata offers panel logit and probit commands:
* Example of reshaping wide data to long format reshape long gdp unemployment, i(country_id) j(year) Use code with caution. gdp and unemployment are the time-varying variables. i(country_id) specifies the unique entity identifier. j(year) creates a new variable indicating the time period. Declaring Panel Structure
Do you expect your key variables to change , or are they static ?
xtreg ln_wage grade age c.age#c.age ttl_exp, re Because the test often produces a negative chi‑squared
Eliminates omitted variable bias caused by time-invariant omitted factors.
xtreg ln_wage hours age tenure, re
The standard summarize command blends all variations together. Use xtsum to decompose the statistics: xtsum gdp investment unemployment Use code with caution.
use union_panel.dta xtset id year xtsum wage union experience