Stata training and workshops

This intensive two-day course provides a practical and applied introduction to local projections (LPs)as a flexible econometric framework for estimating dynamic effects in macroeconomic, financial, and policy-related settings. The course begins with the foundations of local projections in time-series analysis, then expands to panel local projections, state-dependent and time-varying specifications, and finally to LP-DiD approaches for dynamic treatment analysis in staggered intervention settings. Throughout the course, participants will learn both the conceptual logic and the empirical implementation of local projections in Stata, with emphasis on model design, identification, inference, interpretation, and replication. The course combines theory, methodological discussion, and hands-on exercises based on applied examples.

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.

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.