Teaching
Teaching
With Professor Davide Pettenuzzo
Course Description: An introduction to multiple regression techniques with focus on economic applications. Discusses extensions to discrete response, panel data, and time series models, as well as issues such as omitted variables, missing data, sample selection, randomized and quasi-experiments, and instrumental variables. Also develops the ability to apply econometric and statistical methods using computer packages.
Led and managed a teaching team of five graduate teaching fellows and two undergraduate course assistants in preparing weekly section materials for students, grading problem set assignments, and holding office hours and exam review sessions for students.
Assisted lead professor in preparing course materials, slides, problem set assignments, and exams.
Taught one discussion section comprised of 20 students, where I taught course material on linear regression models, discrete choice models, time series models, and panel data models. I also provided extensive instruction on using Stata and R to apply the conceptual materials to hands-on empirical work.
Led group and individual office hours for students to provide additional support in econometric concepts and their empirical application using Stata and R statistical software.
Taught two discussion sections comprised of a total of 40 students, and provided regular office hours and individual tutoring for students.
Provided detailed written feedback to students on problem set assignments.
Wrote an introductory primer and starting code files for helping beginner students with programming and conducting data analysis in Stata.
With Professor Raj Chetty and Professor Gregory Bruich
Course Description: This course will show how "big data" can be used to understand and address some of the most important social and economic problems of our time. The course will give students an introduction to frontier research and policy applications in economics and social science in a non-technical manner that does not require prior coursework in Economics or Statistics, making it suitable both for students exploring Economics for the first time and more advanced students. Topics include equality of opportunity, education, innovation and entrepreneurship, health care, climate change, and crime. In the context of these topics, the course will also provide an introduction to basic methods in data science, including regression, causal inference, and machine learning. The course will include discussions with leading practitioners who use big data in real-world applications.
Led weekly hackathon lab sections for 30-40 students, providing instruction on empirical methodology, discussion of applications to social mobility policies, and technical support on applied data analysis and coding using Stata.
Provided individual and group office hours to assist students with conceptual and technical questions on course materials.
Graded weekly lab assignments and provided detailed feedback to students on their written empirical projects, with special focus on validating causal inference methodology and conceptualizing empirical research designs.
Full teaching evaluations available by request.