Empirical Methods for Applied Economists

3.0 credits · Graduate

The course introduces students to a practical toolkit to enhance empirical economic research in a data-rich, AI-assisted world. The format is hands-on. Students learn in R, building a versioned, reproducible research portfolio on GitHub. The course teaches workflows and tools that are becoming essential to modern empirical research without covering inference. Grading is based on (1) participation, (2) weekly empirical exercises, and (3) a final project. Topics covered include: (1) programming foundations in R; (2) version control, project structure, and reproducibility (git, GitHub, renv, Quarto); (3) responsible use of AI coding tools, verification practices, and disclosure standards; (4) data acquisition (APIs, web scraping, databases); (5) data wrangling and visualization; (6) basic spatial data analysis; and (7) research workflow and communication.