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.
Learn how and when to use Stata’s treatment-effects estimators to analyze treatment effects in observational data. Use regression adjustment, inverse probability weights, doubly robust methods, propensity-score matching, and covariate matching to estimate average treatment effects (ATEs) and ATEs on the treated. We will cover the conceptual and theoretical underpinnings of treatment effects as well as many examples using Stata.
Learn how to use all of Stata's tools and become a sophisticated Stata user. You will understand the Stata environment, how to import and export data from different formats, how Stata's intuitive syntax works, data management in Stata, matching and merging, how to analyze subgroups of data, how to reproduce your work and document it for publication and review, how to interact with the Stata community online, and more.
Become an expert in organizing your work in Stata. Make the most of Stata's scripting language to improve your workflow and create concretely reproducible analyses. Learn how branching, looping, flow of control, and accessing stored estimation results can speed up your work and lead to more complete analyses. Learn about bootstrapping and Monte Carlo simulations, too.
Learn how to communicate your data with Stata's powerful graphics features. This course will introduce different kinds of graphs and demonstrate how to use them for exploratory data analysis. Topics include how to use graphs to check model assumptions; how to format, save, and export your graphs for publication using the Graph Editor.
Become an expert in the analysis and implementation of linear, nonlinear, and dynamic panel-data estimators using Stata. This course focuses on the interpretation of panel-data estimates and the assumptions underlying the models that give rise to them. The course is geared for researchers and practitioners in all fields.
Course registration is binding. Upon cancellation more than 8 working days before the course start date, we invoice 50% of the course fee. Upon cancellation less than 8 working days before the course start date, we invoice the full course fee. Click here to read the full Terms and Conditions!