Schedule

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

Readings include textbook chapters and occasional journal articles
Class materials contain more details, math appendices, and other helpful resourcesThese “online appendices” keep the slides nice and de-cluttered!
Slides are “Xaringan” presentations in html that can be opened in any browserYou can find a downloadable PDF in each respective class page
R materials contain extra tutorials, videos, practice exercises for using R
Assignments are generally due 11:59 PM Sunday

Relevant materials (if applicable, icons will become links) will be posted before class meets.

Last Update: 10:08:59 Tue Nov 17 2020

I. Data Analysis in R	Reading	Class	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 Sun Sept 6
II. Linear Regression and Statistical Inference	Reading	Class	Slides	R	Assignment
2.1 Data 101 and Descriptive Statistics
2.2 Random Variables and Distributions
Problem Set 2 due Sun Sept 13
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 Sunday Sept 27
Midterm Exam week of Sept 28
III. Causal Inference	Reading	Class	Slides	R	Assignment
3.1 The Fundamental Problem of Causal Inference & Potential Outcomes
3.2 Causal Inference II: DAGs
3.3 Omitted Variable Bias
3.4 Multivariate OLS Estimators: Bias, Precision, and Fit
Problem Set 4 due Sun Oct 25
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 Sun Nov 8
IV. Panel Data & Advanced Models	Reading	Class	Slides	R	Assignment
4.1 Panel Data and Fixed Effects Models
4.2 Difference-in-Difference Models
Problem Set 6 (Practice, Ungraded)
Empirical Research Paper Project Due Sunday Nov 22
Final Exam Friday Nov 20—Tuesday Nov 24
4.3 Instrumental Variables Models
4.4 Regression Discontinuity Models
4.5 Binary Dependent Variables Models
4.6 Prediction, Classification, & Machine Learning