Stata Panel Data __top__

collapse (mean) wage experience, by(id)

To check if Random Effects is superior to Pooled OLS, run the Breusch and Pagan Lagrangian multiplier (LM) test after your RE regression: xtreg y x1 x2 x3, re xttest0 Use code with caution. Null Hypothesis ( H0cap H sub 0

Pooled Ordinary Least Squares (OLS) acts as if the panel structure does not exist, simply pooling all observations together.

* Test for cross-sectional dependence xtcsd, pesaran abs * Estimate using Panel-Corrected Standard Errors (PCSE) xtpcse y x1 x2 x3 Use code with caution. Summary Workflow Cheat Sheet Stata Command xtset id time Defines entity and time dimensions Descriptives xtsum varlist Splits variance into within/between components Static Panel xtreg y x, fe / xtreg y x, re Runs fixed or random effects regressions LM Test xttest0 Compares Pooled OLS vs. Random Effects Hausman Test hausman fe_model re_model Compares Fixed Effects vs. Random Effects Robustness vce(cluster id) Controls for heteroskedasticity and autocorrelation Dynamic Panel xtabond / xtdpdsys Estimates models with lagged dependent variables stata panel data

), you fail to reject the null hypothesis. This means you can use the model.

Before any analysis, Stata must know which variable identifies the panel (individual) and which identifies time.

This command calculates the summary statistics for the dependent variable depvar and the independent variable indepvar . collapse (mean) wage experience, by(id) To check if

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

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xtreg wage educ experience union i.year, re Summary Workflow Cheat Sheet Stata Command xtset id

Step 2: Pooled OLS vs. Random Effects (Breusch-Pagan LM Test)

reshape long wage exp, i(id) j(year)