Stata: Panel Data

We model log wages ( ln_wage ) as a function of hours worked, age, and tenure.

Before running any panel models, you must tell Stata that your data is structured as a panel. This is done using the xtset command, which defines the panel variable ( ) and the time variable (

Choosing the right estimation model depends heavily on your assumptions regarding unobserved individual heterogeneity (the unique, unmeasured traits of each entity). Pooled Ordinary Least Squares (OLS) stata panel data

Variation across the distinct entities (ignoring time).

and published his findings, forever grateful for the power of the command suite. specific Stata code for running a Hausman test on your own dataset? We model log wages ( ln_wage ) as

To check balance explicitly:

When the dependent variable is affected by its own past (e.g., current investment depends on last year's investment), standard FE models are biased. Use the estimator. xtabond l_inv l(1/2).l_inv, lags(2) Use code with caution. 5.2. Panel Logit/Probit Pooled Ordinary Least Squares (OLS) Variation across the

Stata’s xt family extends far beyond xtreg . Here are the most important advanced commands:

xtabond wage experience union, lags(1) maxldep(2)

In a Fixed Effects framework, you can test for groupwise heteroskedasticity using a modified Wald test via the user-written command xttest3 : ssc install xttest3 xtreg income education age, fe xttest3 Use code with caution. Dynamic Panel Data (GMM)

A unique variable identifying each cross-sectional unit (e.g., country_id , firm_id , person_id ).