Stata training and workshops

Nearly a century after its first application, the method of instrumental variables remains an essential tool for causal inference in economics, political science, criminology, epidemiology, and beyond. Covering both classic issues and more recent developments, this course will bring you up to speed with instrumental variables methods for microeconomic research through a combination of theory, simulations, and real-data examples in Python and Stata.

Gain a comprehensive understanding of four key elements in Stata: data management, statistical analysis, graphics, and reproducible reporting. Designed for both beginners and users seeking to increase their proficiency, this course equips you with the fundamentals for working efficiently in Stata. By the end of the course, you will be able to import, clean and manage your data, perform basic analyses and visualize results, create customized graphics, and write dynamic reports using Stata.

Join an Introduction to Stata workshop led by Dr. Amanda Eng (Massey University) at the AEA 2026 Annual Scientific Meeting. A hands-on workshop covering Stata, data management, descriptive statistics, and basic epidemiological analysis through guided exercises with a real dataset. You will leave able to write basic Stata commands, manage data, summarise findings, and run simple epi analyses. No Stata experience needed, though basic epidemiology knowledge is helpful.

Explore dynamic and static panel time series models with hands-on Stata applications. This two-day online course introduces participants to time series panel data models, focusing on key challenges such as slope heterogeneity, structural breaks, and cross-sectional dependence. Through intuitive instruction and hands-on Stata exercises, attendees will learn to estimate and interpret static and dynamic models for large panels in applied economics contexts.

This course introduces multilevel/mixed modeling for nested and longitudinal data and its implementation in Stata. Mixed models contain both fixed effects, analogous to regression coefficients, and random effects, effects that vary across clusters. Participants will learn how to use mixed models to answer research questions about the observation- and cluster-level data and how to disaggregate these effects. Introductory theory, estimation, model building, and diagnostics will be discussed and demonstrated through many examples.

This course introduces structural equation modeling and its implementation in Stata. Stata allows for fitting structural equation models in two ways—by using the command syntax or using the SEM Builder to draw path diagrams. Examples will demonstrate both approaches, starting with linear regression up to confirmatory factor analysis and growth curve modeling. Model identification and evaluation will also be extensively discussed. The course concludes with a brief introduction to multilevel models and generalized linear models within the SEM framework.

This course provides an introduction to the theory and practice of panel-data analysis. After introducing the fixed-effects and random-effects approaches to unobserved individual-level heterogeneity, the course covers linear models with exogenous covariates, linear models with endogenous variables, dynamic linear models, and some nonlinear models. A quick introduction to the generalized method of moments estimation technique is also included. The differences between the individual-specific and population-averaged interpretations are discussed throughout the course. Concepts are extensively illustrated using exercises and examples worked in Stata.