TEACHING

ECON 605/605, Macroeconomics Theory I/II

• First year PhD macroeconomics sequence required for all economics PhD students

• Institution: University of Michigan

• Years: 2023-2024, 2024-2025

• Role: Tutor

• Topics Covered: Aggregate Production Functions, Production Side Equilibrium, Solow Model, OLG, Neoclassical Growth Model, Endogenous Growth Model, Dynamic Programming and Value Functions, Search, Endowment Economy, Asset Pricing, Incomplete Markets, Investment Demand, Consumption-Saving, PIH, Real Business Cycle, Sticky Pricing, & New Keynesian Model

• Responsibilities: Teaching weekly review sessions for first-year PhD students (roughly 10-15 each year), developing and distributing comprehensive notes on each topic, preparing practice materials from coursework and prior prelims, and offering supplemental office hours during the academic year and summer

• Prelim Pass Rate: Among the regular attendees of my sessions, 100% and 92% of the students passed the macroeconomics preliminary exam in 2024 and 2025, respectively

• Selected quotes from anonymous student feedback:

  • "Great with answering questions, walking through problems methodically"

  • "Thorough and clear in explaining things, points out the important details"

  • "I really enjoyed how careful Kelcie was to explain concepts and how much active engagement was a part of her pedagogy. I think all too commonly professors zoom past really important parts of a model they see as trivial which are the root of why we all get stumped. Kelcie did a wonderful job of breaking them down into their fundamental pieces in a way I found deeply informative."

  • "Kelcie oozed mastery over all the topics and I found that to be inspiring! You explained complex concepts in a clear and logical way, rationalizing why we need to have each piece of the whole and explaining the basic concepts making up the whole argument. This helped me understand the material better!"

  • "You were always extremely prepared for the session, so I knew you would help with whatever question I had. This also made it feel like a comfortable environment to ask questions even if I thought I was going to sound stupid for asking it."

  • "Kelcie is very approachable and extremely patient. She makes difficult concepts extremely intuitive and accessible. She's very good at emphasizing which parts of the models are important and which ones not to worry as much about. Highlighting potential variations of the model, and why they exist, was also very helpful. She doesn't mind repeating things for those that don't understand the first time. It was really clear she cared about whether we understood the material and how to best approach problems we might see on the prelim."

  • "Kelcie explained concepts concisely and clearly. She did a good job of determining what was important and then focusing on that, allowing us to spend more time actually understanding the material instead of trying to cover everything. She fielded student feedback well and always ensured what we were doing in tutoring was actually helpful for us."

  • "Kelcie was an amazing tutor! Her lessons were always organized and thoughtfully planned, which made the material clearer and easy to digest. Her comprehensive supplementary notes were a great resource to reference and review, and provided helpful references to additional practice problems. The mix of lectures and practice problems not only helped boil down complex material into key concepts, but also allowed me to understand how to apply them in practice. She was great at fielding questions from students, and went above and beyond to make sure they felt confident and prepared. For example, answering extra questions via email or staying later/hosting additional sessions to help. Beyond being an amazing instructor with a lot of knowledge about the material, she provided great advice on test-taking strategies and how to navigate the PhD in general. She was clearly invested in the success of her students, and was always an approachable and encouraging presence inside and outside the classroom. Her tutoring support was critical to my success in the course and prelims exams!"

ECON 3412, Introduction to Econometrics

• Core econometrics course required for all undergraduate economics majors (and many other disciplines as well). Focuses on the empirical application of econometric techniques.

• Institution: Columbia University

• Semesters: Spring 2017, Fall 2018, Spring 2018

• Role: Teaching Assistant 

• Topics Covered: multivariate regressions, non-linear regression models, logit/probit regression models, instrumental variables, analysis of random experiments and quasi-experiments, and time series econometrics

• Responsibilities: Teaching a weekly section for 15-20 students each semester, holding bi-weekly office hours, and grading homework assignments