estimates store fe_result xtreg wage educ experience union i.year, re estimates store re_result hausman fe_result re_result
Panel data has two dimensions of variation: units (variation from one person/country to another) and within a unit (variation over time for the same person/country). Stata provides specialized commands to explore these dimensions. Summary Statistics: xtsum
Panel data often has serial correlation (today's error is correlated with yesterday's).
Fixed Effects (preferred due to ability bias) xtreg wage union experience i.year, fe robust stata panel data
Panel data, also known as longitudinal data, is a type of data that involves observing the same units (e.g., individuals, firms, countries) over multiple time periods. Stata is a popular statistical software package that provides a wide range of tools for analyzing panel data. In this piece, we will cover the basics of panel data in Stata, including data setup, summary statistics, and common panel data models.
Once your data is in the long format, you must explicitly tell Stata that the dataset has a panel structure. This is achieved using the xtset command: xtset id year Use code with caution.
xtreg ln_wage hours age tenure, fe xtcd
: xtabond2 in Stata (user-written, by Roodman) is more flexible than official xtabond . Yet many journals still accept the older command.
Or with vce(cluster id) :
This model was more optimistic. It assumed that the individual differences weren't correlated with education, allowing Sam to study variables that don't change, like gender or race. Step 3: The Great Decision To decide which path to take, Sam summoned the Hausman Test hausman fixed random estimates store fe_result xtreg wage educ experience union i
If cross-sectional dependence is present, standard estimators become inefficient. You can correct for this using Driscoll-Krauss standard errors: xtscc income education experience, fe Use code with caution. 6. Advanced Panel Data Techniques
Panel data contains observations on multiple entities (individuals, firms, countries) observed over multiple time periods. Key Characteristics Cross-sectional dimension (
Are you dealing with a over a short time or vice versa? Fixed Effects (preferred due to ability bias) xtreg
The Random Effects model assumes that the variation across entities is random and uncorrelated with the independent variables included in the model. Unlike FE, RE allows you to include time-invariant variables (like gender) as explanatory variables. xtreg y x1 x2, re Choosing Between FE and RE: The Hausman Test