Teaching

Economics 427 (Intro to Econometrics II) is a hands-on, flipped-format class focused on building practical data analysis skills for causal inference and prediction. Students learn to interpret econometric models, use statistical tools to identify cause-and-effect relationships, make predictions, and communicate results effectively. Students build skills primarily through applied problem sets (with both analytical and coding components) and a substantial term project that follows a full data-analytic workflow from exploration to inference and presentation. Grades are based on three exams, a term project, homeworks, and participation. The course relies on publicly available texts in causal research design and statistical learning, and R (Positron IDE) as the primary software environment.

Economics 628 (Econometrics I) is a first-year Ph.D. course that introduces core probability theory and statistical inference as a foundation for later work in econometric modeling, inference, and prediction. The class runs in a flipped format: students study material in advance, turn in handwritten homework at the start of each meeting, and then use class time to discuss and deepen that day’s topics. Assessment is based primarily on three exams, with additional credit from weekly problem sets and participation. Course communication/announcements are handled through Google Classroom. Alongside the required textbook, the course also uses short R scripts to illustrate theoretical concepts and help students become more comfortable with R.