Schedule

This page contains all of the following resources for each class meeting:

  • Content materials contains suggested readings, more details about assignments, math appendices, and other helpful resources. I suggest you view these before each class.
  • Slides are “Xaringan” presentations in html that can be opened in any browser (You can find a downloadable PDF in each respective content page)
  • R materials contain extra tutorials, videos, practice exercises for using R
  • Assignments are listed with due dates

Please note that the lesson numbers, topics, and titles (e.g. 1.1) are my design, and do not match up with the textbook!

Relevant materials, if applicable will be posted before class meets and become colored links.

I. Data Analysis in R Content Slides R Assignment
Preliminary Survey
1.1 — Introduction to Econometrics
1.2 — Meet R
1.3 — Data Visualization with ggplot2
1.4 — Data Wrangling in the tidyverse
1.5 — Optimize Workflow: Markdown, Projects, and Git
Problem Set 1 due Tues Sep 14
II. Linear Regression and Statistical Inference Content Slides R Assignment
2.1 — Data 101 and Descriptive Statistics
2.2 — Random Variables and Distributions
Problem Set 2 due Tues Sep 21
2.3 — OLS Linear Regression
2.4 — OLS: Goodness of Fit and Bias
2.5 — OLS: Precision and Diagnostics
2.6 — Statistical Inference
2.7 — Inference for Regression
Problem Set 3 due Thurs Oct 7
Midterm Exam Thurs Oct 14
III. Causal Inference Content Slides R Assignment
3.1 — The Fundamental Problem of Causal Inference & Potential Outcomes
3.2 — Causal Inference & DAGs
3.3 — Omitted Variable Bias
3.4 — Multivariate OLS Estimators: Bias, Precision, and Fit
Problem Set 4 due Tues Nov 9
3.5 — Writing an Empirical Paper
3.6 — Regression with Categorical Data
3.7 — Regression with Interaction Effects
3.8 — Polynomial Regression
3.9 — Logarithmic Regression
Problem Set 5 due Tues Nov 23
IV. Panel Data & Advanced Models Content Slides R Assignment
4.1 — Panel Data and Fixed Effects Models
4.2 — Difference-in-Difference Models
Problem Set 6
Empirical Research Paper Project
Final Exam Thurs Dec 9
4.3 — Instrumental Variables Models
4.4 — Regression Discontinuity Models
4.5 — Binary Dependent Variables Models
4.6 — Prediction, Classification, & Machine Learning