ben h. williams professor of economics
baylor university
Spring 2026 — Harvard University
Instructor: Scott Cunningham
Email: scunningham@fas.harvard.edu
Meeting: Mon & Wed, 1:30–2:45 PM
Office Hours: TBD
Teaching Fellow: Kaixiao Liu
TF Email: kaixiaoliu@g.harvard.edu
Section: TBD
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 (5 bi-weekly) | 30% |
| Midterm Exam (in-class) | 40% |
| Final Exam (in-class) | 30% |
Note: 70% of your grade comes from in-class exams. Problem sets are for learning; exams are for assessment.
Problem sets are due Fridays at 11:59 PM. Each includes analytical problems and R simulation components.
| Assignment | Due Date | Topics | Files |
|---|---|---|---|
| Problem Set 1 | Feb 13 | Probability, conditional probability, Bayes' rule | |
| Problem Set 2 | Feb 27 | Random variables, expectation, variance, CEF | |
| Problem Set 3 | Mar 13 | Sampling, CLT, estimation, hypothesis testing | |
| Problem Set 4 | Apr 10 | OLS introduction, mechanics, properties | |
| Problem Set 5 | Apr 24 | Multiple regression, OVB, interactions, inference |
| Week | Dates | Topic | Readings | Slides | R Scripts |
|---|---|---|---|---|---|
| 1 | Jan 27, 29 | Introduction; Probability Foundations | A&M 1.1; Blackwell 2.1 | Intro, Probability | |
| 2 | Feb 3, 5 | Random Variables and Distributions | A&M 1.2; Blackwell 2.2–2.3 | RVs, Distributions | Distributions |
| 3 | Feb 10, 12 | Expected Value and Variance | A&M 2.1, 2.2.1–2.2.2; Blackwell 2.4–2.5 | E[X], Var | |
| 4 | Feb 17, 19 | Joint Distributions and the CEF | A&M 1.3, 2.2.3–2.2.4; Blackwell Ch. 1 | Joint, CEF | |
| 5 | Feb 24, 26 | From Population to Sample (LLN, CLT) | A&M 3.1–3.2; Blackwell Ch. 3 | Sampling, CLT | CLT |
| 6 | Mar 3, 5 | Estimation and Confidence Intervals | A&M 3.2.3, 3.3.1; Blackwell Ch. 2 | Estimation, CIs | CIs |
| 7 | Mar 10, 12 | Hypothesis Testing and Power | A&M 3.3.2–3.3.3, 3.4.3; Blackwell Ch. 4 | Testing, Power | Testing, Power |
| Spring Break: March 14–22 | |||||
| 8 | Mar 23, 25 | Advanced Asymptotics (Delta Method, Slutsky) | A&M 3.2; Blackwell 3.5–3.6 | Asymptotics, Delta | |
| TBD | MIDTERM EXAM (end of Week 8 or start of Week 9) | ||||
| Week | Dates | Topic | Readings | Slides | R Scripts |
|---|---|---|---|---|---|
| 9 | Mar 31, Apr 2 | What Is Regression? (BLP, OLS intro) | Blackwell Ch. 5; A&M 2.2.4; MHE 3.1 | BLP, OLS Intro | BLP, OLS |
| 10 | Apr 7, 9 | OLS Mechanics and Properties | Blackwell Ch. 6–7; A&M 4.1 | Mechanics, Properties | |
| 11 | Apr 14, 16 | Multiple Regression and OVB | Blackwell Ch. 6; MHE 3.1.3, 3.2.2 | Multiple, OVB | OVB |
| 12 | Apr 21, 23 | Interactions, Nonlinearities, F-tests | Blackwell Ch. 7; A&M 4.2 | Interactions, F-tests | Interactions |
| 13 | Apr 28, 30 | Robust and Clustered Standard Errors | A&M 4.1.4, 3.5; MHE 8.2 | Robust SE, Clustering | Robust, Cluster |
| 14 | May 5 | Variance Weights and Regression Adjustment | Angrist (1998); Sloczyński (2022) | Weights, Reg Adj | |
| TBD | FINAL EXAM (last day of class or exam 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.
You may discuss problem sets with classmates, but you must write your own solutions and code independently. List all collaborators on your submission.
Problem sets lose 10% per day late. Extensions require advance approval.