This is a core course for Master of Professional Studies (MPS) in Applied Economics and Management. It provides students with the opportunity to explore strategies for behavioral, quantitative, and qualitative problem-solving projects. We consider the conceptual challenges associated with identifying and defining a project topic and examine the practical tasks of selecting or collecting data, analyzing data, and reporting the results, including visualization and writing. The objective of the course is for students to understand how problems associated with different kinds of projects can be addressed with empirical methods. Many course activities will be structured as teamwork, and team leadership and management skills are a major component of the course. The course topics are introduced through readings, class discussion, and independent team-based research.
| Date | Topic | Due | |
|---|---|---|---|
| Getting started | Wed Aug 26 | 1 · Introductions Who is in the room, and how the course runs. AEM 6991 produces a plan and AEM 6992 executes it, so everything you commit to this fall you live with in the spring. Then an afternoon of groups working out what they would like to explore, because I form the teams from what you tell each other. | Research interest survey, on Canvas before meeting 2 |
| Foundations | Wed Sep 2 | 2 · Theory and causality A theory is a set of assumptions that produces a prediction you could be wrong about. Empirical work can settle part of that and never all of it. Then causality proper: every comparison stands in for a world that did not happen, and the graph tells you which variables to condition on and which to leave alone. | Your team's written ground rules — agreed and handed in today |
| Wed Sep 9 | 3 · Finding the question Finding the question is the job, not the preamble to it. Where ideas come from, what makes an answer worth something to somebody, five shapes a project takes, and a map of what your question needs against what your team has. Then you map a shared case, and then your own. | ||
| Wed Sep 16 | 4 · Ethics and access Two gates stand between your design and your data, and they are different kinds of thing. The IRB is an ethics review. A data-use agreement is a contract. Both take longer than teams expect, which is why this meeting sits in September rather than in November. | ||
| Wed Sep 23* | 5 · Coding tools and AI Every serious analysis is code, whether or not you wrote it. "Use AI" is four different things and they fail differently — only one of them produces output you can check. Then the practical business of getting a file at all: what Cornell already pays for, what an API is, and why scraping is the last resort dressed up as the clever one. | ||
| Methods | Wed Sep 30 | 6 · Models and uncertainty What the standard models estimate, and how to read a coefficient in the units of the problem. Where uncertainty comes from, and what a p-value is not. Then the distinction that decides half your projects: explaining or predicting. | Rate your teammates |
| Wed Oct 7 | 7 · Model failures Estimates fail in two ways. Bias moves the number: selection and sorting, omitted variables, measurement error, reverse causality. False precision leaves the number alone and shrinks the error bar around it. Then the fix everybody reaches for, and its ceiling — 'I added controls' is a claim about the graph, not a technique. | ||
| Wed Oct 14 | 8 · Research designs I Experiments, instrumental variables, regression discontinuity. What variation each exploits, what each assumes, and how each fails. | ||
| Wed Oct 21* | 9 · Research designs II Difference-in-differences, panel data with fixed effects, event studies. The question is the same each time: which identifying variation survives the fixed effects, and what assumption is left carrying the estimate. | Project pitch — one page, presented in class | |
| Communication | Wed Oct 28 | 10 · Writing Writing is taught here, as a method rather than as advice. A piece of writing is built outline first — one topic sentence per paragraph, in order, before any prose exists. The fastest way to see that is backwards. | |
| Wed Nov 4 | 11 · Figures Which figure answers which question, and why — distributions, time, space, comparison. Then what figures hide: aggregation, scale, binning, projection, area scaled by radius, chosen windows, counts where you needed a rate. Then the hard half nobody practises — an honest figure that still makes a point. | ||
| Wed Nov 11 | 12 · Speaking A talk is an outline with pictures. One claim per slide, and the claim is the slide title, not a label. Then the genre you actually face: pitching a research plan to people who will never read the paper, and who want to know what it costs and what they would do differently. | Proposal outline · Rate your teammates | |
| Delivery | Wed Nov 18* | 13 · Midterm The midterm, and nothing else. You are given a situation and asked to design the study that would answer it, in writing. Class ends when you hand it in. | |
| Wed Nov 25 | Thanksgiving recess — no class | ||
| Wed Dec 2 | 14 · The pitch Final presentations, and each team reviews another team's work. Then what your team owes AEM 6992 over the break. | Final presentation, in class | |
| Fri Dec 11 | Research proposal due — the final write-up. Date to be confirmed |
* These dates are not final: Sep 23 — Instructor traveling — this meeting is held remotely · Oct 21 — Instructor traveling — moves to Oct 20 or Oct 22; date to be confirmed · Nov 18 — Instructor off campus — the midterm is proctored; arrangements announced by week 10