Teaching

Economics 427 (Intro to Econometrics II) is a hands-on, flipped-format class built around a single text, Nick Huntington-Klein’s The Effect: An Introduction to Research Design and Causality, covered cover to cover. Students learn to translate an empirical question into a research design, reason through causal diagrams, and estimate treatment effects using regression, matching, fixed effects, event studies, difference-in-differences, instrumental variables, and regression discontinuity — implementing every method in R with the tidyverse and modern causal-inference packages (fixest, did, rdrobust, MatchIt, and others). The semester culminates in an original student research project that applies these identification strategies to a topic and dataset of the student’s own choosing. Grades are based on two midterms, a final exam, the course project, homework, and participation. R (via Positron) is 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.

Economics 630 (Econometrics III: Prediction, Time Series, and Causal Inference) is a graduate course giving students a working command of the two dominant empirical traditions in modern economics. The first two-thirds of the semester build a supervised- and unsupervised-learning toolkit (following An Introduction to Statistical Learning) and a time-series forecasting toolkit (ARMA, unit roots, VAR, cointegration), before the final third pivots from “what will happen?” to “what causes what?” with Nick Huntington-Klein’s The Effect — causal diagrams, treatment effects, regression as causal adjustment, matching, fixed effects, event studies, difference-in-differences, instrumental variables, and regression discontinuity. The class runs in a flipped format in which enrolled students take turns leading sessions with a short lecture, building teaching and presentation skills alongside the technical material. Assessment is based on two midterms, a final exam (causal material only), weekly homework, and presentation/participation. R (tidymodels/tidyverse) is used throughout, alongside standard machine-learning, time-series, and causal-inference packages.