Chris Conlon

PhD Econometrics

Panel Data Econometrics- 2024

Syllabus

Github Page (for .tex files)

Elements of Statistical Learning (Textbook)

Bruce Hansen's Econometrics (Textbook)

Lecture Notes

Lecture 1: Review and Simulation Methods
Exercises Notebook
- Review of Fall Semester [Notes] [Video]
- Time Series [Notes]
- Simulation Methods [Notes] [Video]
Lecture 2: Maximum Likelihood Estimation
Exercises Notebook
- Part a: Intro [Notes] [Video]
- Part b: Optimization Details [Notes] [Video]
- Part c: Examples [Notes] [Video]
- Part d: Binary Choice [Notes]
Lecture 3: Generalized Method of Moments (GMM)
Exercises Notebook
- Part a: Intro and Theory [Notes] [Video]
- Part b: Examples and Variance Estimation [Notes] [Video]
- Part c: Advanced Topics and Practical Issues [Notes] [Video]
- Part d: Hypothesis Testing [Notes] [Video]
Lecture 4: Delta Method, Bootstrap, and Cross Validation
Exercises Notebook
- Part a: Delta Method [Notes] [Video]
- Part b: Bootstrap and Subsampling [Notes] [Video]
- Part c: Cross Validation [Notes] [Video]
Lecture 5: Intro to Nonparametrics
Code Examples
- Part a: Polynomials and Sieves [Notes] [Video]
- Part b: Splines [Notes] [Video]
- Part c: k-Nearest Neighbor [Notes] [Video]
- Part d: Curse of Dimensionality and Kernels [Notes] [Video]
- Part e: Advanced Topics [Notes] [Video]
Lecture 6: Model Selection and Regularization
Exercises Notebook
- Part a: AIC, BIC, and KLIC [Notes] [Video]
- Part b: Stepwise and Stagewise Regression [Notes] [Video]
- Part c: Penalized Regression (LASSO and Ridge) [Notes] [Video]
- Part d: Principal Components [Notes] [Video]
Lecture 7: Multiple Discrete Choice
[Train's Book] Exercises Notebook
- Part a: IIA Logit [Notes] [Video]
- Part b: Nested Logit [Notes] [Video]
- Part c: Mixed Logit [Notes] [Video]
Lecture 8: Advanced Binary Choice
- Part a: Issues with LPM, Incidental Parameters Problem [Notes] [Video]
- Part b: Binary Choice with Endogeneity: Control Functions, Special Regressors, etc. [Notes] [Video]
Lecture 9: Program Evaluation
Exercises Notebook
- Part a: Potential Outcomes [Notes] [Video]
- Part b: Parametric Selection [Notes] [Video]
- Part c: k-NN Matching [Notes] [Video]
- Part d: Propensity Scores and IPW [Notes] [Video]
Lecture 10: Instrumental Variable and LATE
Exercises Notebook
- Part a: Local Average Treatment Effects (LATE) [Notes] [Video]
- Part b: Application of LATE to Demand [Notes] [Video]
- Part c: Marginal Treatment Effects (MTE) [Notes] [Video]
Lecture 11: Panel Approaches to Treatment Effects
- Part a: Regression Discontinuity Design (RDD) [Notes] [Video]
- Part b: Difference in Difference [Notes] [Video]
- Part c: Synthetic Control [Notes] [Video]
Lecture 12: Panel Approaches Continued
- Part a: Dynamic Panel [Notes] [Video]
- Part b: Empirical Bayes Shrinkage [Notes] [Video]
- Part c: Fixed Effect as Parameters of Interest? [Notes] [Video]
Lecture 13: Recent Issues in 2WFE and Diff-in Diff Designs
- Part a: k-means Clustering and Group Fixed Effects [Notes] [Video]
- Part b: Two-Way FE and Diff-in-Diff [Notes] [Video]
Bonus: Additional Machine Learning
- Part a: Trees, Forests, Boosting, and Bagging [Notes] [Video]

Assignments

Homework 1: Due March 2
Homework 2: Due March 20
Homework 3: Due April 6
Homework 4: RDD and Synthetic Control Due May 5