ben h. williams professor of economics
baylor university
Spring 2026 — Harvard University
Instructor: Scott Cunningham
Email: anthony_cunningham@fas.harvard.edu
Meeting: Mon & Wed, 1:30–2:45 PM, Sever 208
Office Hours: Mon & Wed, 3:00–5:00 PM
CGIS Knafel Building, Room 402 (1737 Cambridge St)
By appointment only: Sign up via Calendly
Teaching Fellow: Kaixiao Liu
TF Email: kaixiaoliu@g.harvard.edu
Sections:
Fridays 10:30–11:45 AM (LISE 303)
Fridays 1:30–2:45 PM (Boylston 104)
TF Office Hours: Sign up via Calendly
This course provides a rigorous foundation in quantitative social science methods for first-year PhD students. After reviewing basic probability theory, we offer a systematic introduction to statistical inference and linear regression—the workhorse tools for empirical research in political science.
We take a "population-first" approach: define what you want to know about the population before worrying about estimation. Probability is the language for describing populations; statistics is the machinery for learning about them from samples.
| Component | Weight |
|---|---|
| Problem Sets (weekly) | 30% |
| Midterm Exam (in-class, Week 7) | 35% |
| Final Exam (in-class) | 35% |
Note: 70% of your grade comes from in-class exams. Problem sets are for learning; exams are for assessment.
Problem sets are due Tuesdays at 11:59 PM. Each includes analytical problems and R simulation components. Weekly problem sets help you practice and internalize the material before exams.
| Assignment | Due Date | Topics |
|---|---|---|
| Problem Set 1 | Tue Feb 3, 11:59 PM | Probability, conditional probability, Bayes' rule |
| Problem Set 2 | Tue Feb 10 | Random variables, expectation, variance |
| Problem Set 3 | Tue Feb 17 | TBD |
| Problem Set 4 | Tue Feb 24 | TBD |
| Problem Set 5 | Tue Mar 3 | TBD |
| Spring Break: March 14–22 | Midterm Week 7 (before break) | ||
| Problem Set 6 | Tue Mar 24 | TBD |
| Problem Set 7 | Tue Mar 31 | TBD |
| Problem Set 8 | Tue Apr 7 | TBD |
| Problem Set 9 | Tue Apr 14 | TBD |
| Problem Set 10 | Tue Apr 21 | TBD |
| Problem Set 11 | Tue Apr 28 | TBD |
| Week | Dates | Topic | Readings | Slides | R Scripts |
|---|---|---|---|---|---|
| 1 | Jan 26, 28 | Introduction; Probability Foundations | A&M 1.1; Blackwell 2.1 | Introduction | Probability | |
| 2 | Feb 2, 4 | Random Variables; Expectation and Variance | A&M 1.2, 2.1; Blackwell 2.2–2.5 | Random Variables | |
| 3 | Feb 9, 11 | Famous Distributions (two lectures) | A&M 1.2–1.3; Blackwell 2.2–2.3 | ||
| 4 | Feb 16, 18 | Joint Distributions; Conditional Expectation and LIE | A&M 2.2.3–2.2.4; Blackwell Ch. 1, 3 | ||
| 5 | Feb 23, 25 | Sampling and Estimation Framework; Plug-in Estimator | A&M 3.1; Blackwell Ch. 3 | ||
| 6 | Mar 2, 4 | MLE; Asymptotics and LLN | A&M 3.2, Ch. 5; Blackwell Ch. 3 | ||
| 7 | Mar 9, 11 | CLT, Confidence Intervals, Delta Method; MIDTERM (Wed Mar 11) | A&M 3.2.3, 3.3.1; Blackwell Ch. 2 | ||
| Spring Break: March 14–22 | |||||
| Week | Dates | Topic | Readings | Slides | R Scripts |
|---|---|---|---|---|---|
| 8 | Mar 23, 25 | Hypothesis Testing; Power and Bootstrap | A&M 3.3.2–3.3.3, 3.4.3; Blackwell Ch. 4 | ||
| 9 | Mar 30, Apr 1 | What Is Regression? (BLP, OLS intro) | Blackwell Ch. 5; A&M 2.2.4; MHE 3.1 | ||
| 10 | Apr 6, 8 | OLS Mechanics and Properties | Blackwell Ch. 6–7; A&M 4.1 | ||
| 11 | Apr 13, 15 | Multiple Regression and OVB | Blackwell Ch. 6; MHE 3.1.3, 3.2.2 | ||
| 12 | Apr 20, 22 | Interactions; Robust and Clustered SEs | Blackwell Ch. 7; A&M 4.1.4, 4.2; MHE 8.2 | ||
| 13 | Apr 27, 29 | Variance Weights and Regression Adjustment | Angrist (1998); Sloczyński (2022); A&M 7.3 | ||
| May 7–16 | FINAL EXAM (during finals period) | ||||
Do not use AI assistants (ChatGPT, Claude, Copilot, etc.) on problem sets. Work with your classmates instead. The learning happens when you struggle through confusion. The 70% of your grade that comes from in-class exams will reveal whether you actually understand the material.
Certain assignments in this course will permit or even encourage the use of generative artificial intelligence (GAI) tools such as ChatGPT. The default is that such use is disallowed unless otherwise stated. Any such use must be appropriately acknowledged and cited. It is each student’s responsibility to assess the validity and applicability of any GAI output that is submitted; you bear the final responsibility. Violations of this policy will be considered academic misconduct. We draw your attention to the fact that different classes at Harvard could implement different AI policies, and it is the student’s responsibility to conform to expectations for each course.
You may discuss problem sets with classmates, but you must write your own solutions and code independently. List all collaborators on your submission.
You have 2 late days total for the semester to use at your discretion—no approval needed. After your late days are used, problem sets lose 10% per day late. Additional extensions require advance approval.