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.

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 





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 





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 





4.1 — Panel Data and Fixed Effects Models 




4.2 — DifferenceinDifference 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 



