Full course syllabus is here.
| Class | Date | Topics to Cover | Pre-class reading | Due |
|---|---|---|---|---|
| 1 | Tue Aug 23 | Course Intro/Productivity/Computational Tools | Julia reference slides | |
| 2 | Thu Aug 25 | Clean code | Gentzkow & Shapiro's handbook | |
| 3 | Tue Aug 30 | Coding Day - go over PS 1 | PS 1 | |
| 4 | Thu Sep 1 | What is structural modeling? | Lewbel (2019 ) Sections 1, beginning of Section 5, and 5.1 | Reading Quiz |
| 5 | Tue Sep 6 | Structural modeling process | Keane YouTube talk | |
| 6 | Thu Sep 8 | Random Utility Models & Logit | Train, Ch. 1-2, 3.1-3.3, 3.7-3.8 | Reading Quiz |
| 7 | Tue Sep 13 | Coding Day - go over PS 2 | PS 2 | |
| 8 | Thu Sep 15 | GEV | Train, 4.1-4.2 | Reading Quiz |
| 9 | Tue Sep 20 | Coding Day - go over PS 3 | PS 3 | |
| 10 | Thu Sep 22 | Mixed Logit, Finite mixture models, EM algorithm | Train, 6.1-6.3, Ch. 14 | Reading Quiz |
| 11 | Tue Sep 27 | Coding Day - go over PS 4 | PS 4 | |
| 12 | Thu Sep 29 | Dynamic choice models | Rust (1987) | Reading Quiz |
| 13 | Tue Oct 4 | Coding Day - go over PS 5 | PS 5 | |
| 14 | Thu Oct 6 | Estimating dynamic models without solving | Hotz & Miller (1993); Arcidiacono & Miller (2011) | Reading Quiz |
| 15 | Tue Oct 11 | Coding Day - go over PS 6 | PS 6 | |
| 16 | Thu Oct 13 | Simulated Method of Moments | Train, 10.1-10.2; Smith, p. 1 | Reading Quiz |
| 17 | Tue Oct 18 | Coding Day - go over PS 7 | PS 7 | |
| 18 | Thu Oct 20 | Model Fit, Counterfactuals, Model validation | Fu, Grau and Rivera (2020), Lang and Palacios (2018) | Reading Quiz |
| 19 | Tue Oct 25 | Subjective Expectations, Stated Preference and Choice Experiments | Train, 7.2-7.3; Koşar, Ransom and van der Klaauw (2022), section 3.3 | Reading Quiz |
| 20 | Thu Oct 27 | Measurement Error & Factor Models | Heckman, Stixrud and Urzua (2006) | Reading Quiz |
| 21 | Tue Nov 1 | Coding Day - go over PS 8 | PS 8 | |
| 22 | Thu Nov 3 | Learning models | Miller (1984) | |
| 23 | Tue Nov 8 | Constrained optimization and equilibrium models | Start finding a paper for presentation/referee report | Take-home Midterm |
| 24 | Thu Nov 10 | Obtaining causal effects without an "identification strategy" | Altonji, Elder & Taber (2005) | Reading Quiz |
| 25 | Tue Nov 15 | DAGs and do-Calculus | Mixtape, Ch. 3 | Reading Quiz |
| 26 | Thu Nov 17 | Potential Outcomes, ATE, LATE, and Control Functions | Mixtape, Ch. 4 | Reading Quiz |
| --- | Tue Nov 22 | No class | ||
| --- | Thu Nov 24 | No class (Thanksgiving) | ||
| 27 | Tue Nov 29 | Intro to Machine Learning | James et al., section 2.1 (pp. 15-29) | Reading Quiz |
| 28 | Thu Dec 1 | Machine Learning for Causal Modeling | Work on Referee Report & Presentation | |
| 29 | Tue Dec 6 | Presentations or Time Series Intro (depending on time) | Presentation | |
| 30 | Thu Dec 8 | Presentations | Presentation, Referee Report | |
| --- | Mon Dec 12 | Final Exam (Referee Report due) | Research Proposal |