xtvfreg: A Stata command for modeling mean and variance in panel data

Standard panel-data models assume homoskedasticity, estimating only how covariates shift the mean of an outcome while treating variance as constant across observations. This assumption can obscure important dimensions of inequality, particularly when the dispersion of outcomes—not just their average level-differs systematically across groups or individuals. This presentation introduces xtvfreg, a community-contributed Stata command that jointly estimates mean and variance equations for panel data using an iterative weighted generalized least-squares (GLS) procedure. Building on the framework proposed and demonstrated for answering a substantive question in Mooi-Reci and Liao (2025, European Sociological Review), xtvfreg allows researchers to model not only who has higher or lower outcomes (via the mean equation) but also who experiences greater or lesser dispersion in those outcomes (via the variance equation), separated by group when necessary. The presentation demonstrates the command's syntax, walks through its iterative estimation algorithm, and provides a step-by-step application using the NLSY women's wage panel data. Attendees will leave with the knowledge to implement xtvfreg in their own research and to think more carefully about variance as a substantive outcome of interest, not merely a nuisance parameter.