2026 Stata Sociology Virtual Symposium

geoplot is a powerful Stata command for drawing maps from shape files and other datasets. Multiple layers of elements such as regions, borders, lakes, roads, labels, and symbols can be freely combined and the look of elements (e.g. their color) can be varied depending on the values of variables. Compared to previous solutions in Stata, geoplot provides more user convenience, more functionality, and more flexibility. In this talk I will give an overview of the command and illustrate its use with examples.

An Agent Based Model (ABM) is a simulation in which agents that each follow simple rules interact with one another and thus produce an often surprising outcome at the macro level. The purpose of an ABM is to explore mechanisms through which actions of the individual agents add up to a macro outcome by varying the rules that agents have to follow or varying with whom the agent can interact (for example, varying the network). These models have many applications, like the study of segregation of neighborhoods, the adoption of new technologies, or the spread of a disease. In this talk I will discuss the -abm- package. This package contains a number of Mata classes intended to help manage various aspects of an Agent Based model. Implementing a new ABM will always require that person developing the ABM does some programming, but many tasks will be similar across ABMs. For example, in many ABMs the agents live on a square grid (like a chessboard), and can only interact with their neighbours. The abm_grid class, which is part of the -abm- package contains a set of Mata functions that will do those tasks. Alternatively, the agents could live in a network, and the abm_nw class is there to handle that case. The talk will begin with a brief introduction to Mata and classes in Mata and than discuss how to use the classes in the -abm- package to create a ABM by going through various examples.

Comparing effects is a common task for the applied data analyst. For example, tests of interaction involve comparing the effects of one variable at multiple levels of another variable. Comparisons of effect sizes within models involve quantifying each focal variable?s effect and testing their equality. Cross-model comparisons are also common, for example when comparing processes across different groups. Tests of attenuation like mediation similarly involve comparisons across multiple models. Despite their ubiquity, such tests have challenges: coefficients that don?t quantify effects in the metric of interest, rescaling of coefficients in nonlinear/categorical models, and predictors on differing metrics. In this presentation, I detail the statistical underpinnings of within- and across-model comparisons, present a marginal effects framework that allows for comparisons across most any regression model or predictor type, and show how to automate these tests using the new suest2, mecompare, meinequality, and totalme commands. Compared to existing software, the new commands can be used for: (1) single or multiple models; (2) single-level, multilevel, and longitudinal models; (3) any amount of change for continuous predictors; (4) summary measures for nominal and ordinal variables; (5) comparisons across variables on different metrics; (6) comparisons across different model types; and (7) custom and nonstandard tests of effects. I demonstrate the utility of the approach and the new commands using publicly available social science data.

Abstract TBA.

Standard panel data models assume homoscedasticity, 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 user-written 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), separately by group when necessary. The presentation demonstrates the commands syntax, walks through its iterative estimation algorithm, and provides a step-by-step application using the NLSY womens 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..