Chris Conlon

PhD Econometrics - Fall 2026

Thursdays 9:00am-12:00pm, Backus Conference Room (KMC 7-191)

Syllabus

Github Page (for .tex files)

Textbooks and readings:

• Bruce Hansen's Econometrics (authoritative reference)
• Goff's Causal Inference in Econometrics notes (free; the bridge text)
• Cunningham's Causal Inference: The Mixtape (free online; assigned reading for the research-design weeks)
• Wager's Causal Inference: A Statistical Learning Approach (free draft; a reference for the Spring machine-learning material)

Each session has an in-class workbook (.Rmd): open it in RStudio, reproduce the code from lecture, and work the short problems.

Lecture Notes

• Session 1 (9/3): Probability and Statistics, by Simulation
  
  • Course Introduction [Notes]
  • Probability and the Algebra of Expectations [Notes]
  • How Averages Behave (Asymptotics by Simulation) [Notes]
  • Linear Algebra Review (self-study) [Handout]
  • Research Workflow [Notes]
  • [In-Class Workbook]
• Session 2 (9/10): Linear Regression
  
  • Linear Regression [Notes]
  • Transformations: Logs, Odds, and Interpretation [Notes] [In-Class Workbook]
  • [In-Class Workbook]
• Session 3 (9/17): Inference and Standard Errors
  
  • Variance of OLS and Standard Errors [Notes] [R Code] [Python Code]
  • Hypothesis Testing [Notes]
  • [In-Class Workbook]
• Session 4 (9/24): Applied Regression Workshop
  
  • The Wage Equation End-to-End [Notes] [Code] [Data]
  • [In-Class Workbook]
• Session 5 (10/1): Causality, DAGs, and Experiment Design
  
  • Program Evaluation and Randomized Experiments [Notes] [Power Calculation Code]
  • Causal Diagrams (DAGs) [Notes]
  • [In-Class Workbook]
• Session 6 (10/8): Instrumental Variables I
  
  • Endogeneity, Simultaneity, and 2SLS [Notes] [Code] [Overidentification Notebook]
• Session 7 (10/15): Instrumental Variables II
  
  • Weak Instruments, LATE, Judge Designs, and Shift-Share [Notes] [R]
  • [In-Class Workbook]
• Session 8 (10/22): Panel Data and Event Studies
  
  • Panel Data and Fixed Effects [Notes]
  • Event Studies (panel, and earnings-announcement CARs) [Notes]
  • Simulation code behind the figures [R]
  • [In-Class Workbook]
• Session 9 (10/29): Difference-in-Differences
  
  • Difference in Difference [Notes]
  • Two Way Fixed Effects and Staggered Adoption [Notes]
• Session 10 (11/5): Regression Discontinuity
  
  • Regression Discontinuity [Notes]
• Session 11 (11/12): Matching and Synthetic Control
  
  • Matching Designs [Notes]
  • Synthetic Control [Notes]
• Session 12 (11/19): Maximum Likelihood, Binary Choice, and Selection
  
  • Intro to MLE [Notes]
  • Logit and Probit [Notes] [Notebook] [Code]
  • Selection Corrections [Notes] [Code]
• Session 13 (12/3): Limited Dependent Variables: Censored Outcomes, Durations, and Counts
  
  • Censored Outcomes and the Tobit Model [Notes]
  • Duration Models: Hazards, Kaplan-Meier, Weibull, and Cox [Notes]
  • Count Data: Poisson, Negative Binomial, Zero Inflation, and Poisson Pseudo-ML with Fixed Effects [Notes]
  • Simulation code behind the figures [R]
  • [In-Class Workbook]
• Session 14 (12/10): Final Project Presentations

Assignments

• [Assignment 1] Due 9/17
• [Assignment 2] Due 9/24 [Data]
• [Assignment 3] Due 10/8
• [Assignment 4] Due 10/22
• [Assignment 5] Due 11/5
• [Assignment 6] Due 11/19
• [Assignment 7] Due 12/3