The Modern Stata Playbook: Critical Enhancements You Need to Know
1h 52mIntermediate2026-02-02
Authors
Franz Buscha
Professor of Economics at the University of Westminster
Course details
In this course, Franz Buscha—a professor of economics who has taught economics, statistics, and policy evaluation—explores the biggest feature additions in Stata versions 16 through 19. Dive into cutting-edge statistical tools (Lasso, high-dimensional fixed effects, Bayesian enhancements) as well as the powerful causal-inference suite in Stata. Learn to craft modern graph styles, heat maps, and publication-quality tables. Discover how to manage multiple datasets with frames, run Python and H2O workflows inside Stata, and use ChatGPT for workflow improvements. Plus, learn how to increase your productivity with Do-file Editor enhancements and reproducible reports. This course offers you an updated playbook for applying the newest features in Stata directly to your own projects.
Learning objectives
Apply advanced statistical methods including lasso regression, high-dimensional fixed effects models, and Bayesian analysis to analyze complex datasets in Stata.
Implement causal inference techniques such as difference-in-differences analysis and treatment effects estimation to evaluate policy interventions and program impacts.
Create customized visualizations including heat maps and color-coded graphs to effectively communicate patterns and relationships in data.
Integrate multiple data sources using frames and external tools like Python and H2O to develop comprehensive analytical workflows.
Design reproducible research reports that automatically generate documentation in various formats while maintaining consistency across outputs.
Evaluate the appropriateness of different Stata techniques for specific analytical challenges and select optimal approaches based on data characteristics and research questions.
Learning objectives
Apply advanced statistical methods including lasso regression, high-dimensional fixed effects models, and Bayesian analysis to analyze complex datasets in Stata.
Implement causal inference techniques such as difference-in-differences analysis and treatment effects estimation to evaluate policy interventions and program impacts.
Create customized visualizations including heat maps and color-coded graphs to effectively communicate patterns and relationships in data.
Integrate multiple data sources using frames and external tools like Python and H2O to develop comprehensive analytical workflows.
Design reproducible research reports that automatically generate documentation in various formats while maintaining consistency across outputs.
Evaluate the appropriateness of different Stata techniques for specific analytical challenges and select optimal approaches based on data characteristics and research questions.
Concepts
Introduction
- Level up with Stata and gen AI
New Statitical Capabilities
- Lasso regression
- High-dimensional fixed effects (HDFE)
- Correlated random effects model
- Creating customizable tables
- Bayesian analysis enhancements
Causal Inference
- Difference-in-differences analysis
- Extended regression models (ERM)
- Estimating treatment effects
- Causal mediation analysis
Graphics and Visualisations
- Exploring new graph styles
- Using variables to color graphs
- Creating heat maps
Enhanced Integration
- Managing multiple datasets with frames
- Integrating Python into workflows
- Machine learning with H2O integration
Productivity and Workflow Improvements
- Do-file Editor enhancements
- Creating reproducible reports