Schedule

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

Readings include textbook chapters and occasional journal articles
Assignments are due by the beginning of class unless otherwise stated
Class materials contain extra explanations, math/statistical properties & proofs, 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 browserFile > Print > Save as PDF if you wish to download them
R materials contain extra tutorials, videos, practice exercises for using R

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

Last Update: 16:57:32 Sat Dec 14 2019

1. Data Analysis in R	Reading	Class	Slides	Assignment	R
Preliminary Survey
1.1 Introduction to Econometrics
1.2 The Quest for Causality
1.3 Meet R
1.4 Data Visualization with ggplot2
1.5 Data Wrangling in the tidyverse
1.6 Optimize Workflow: Projects, Markdown, and Git
Problem Set 1 due Sept 19
2. Linear Regression	Reading	Class	Slides	Assignment	R
2.1 Data 101 and Descriptive Statistics
2.2 Random Variables and Distributions
Problem Set 2 due Sept 26
2.3 OLS Linear Regression
2.4 OLS: Goodness of Fit and Bias
2.5 OLS: Precision and Diagnostics
2.6 Inference for Regression
Problem Set 3 due Oct 10
Midterm Exam Thurs Oct 17
3. Extensions to Regression	Reading	Class	Slides	Assignment	R
3.1 Omitted Variable Bias
3.2 Multivariate OLS Estimators: Bias, Precision, and Fit
3.3 Model Specification Strategies
3.4 Multivariate Regression Examples
3.5 Causal Inference and DAGs
Problem Set 4 due Nov 21
3.6 Regression with Categorical Data
3.7 Regression with Interaction Effects
3.8 Polynomial Regression
3.9 Logarithmic Regression
Problem Set 5
4. Panel Data Models	Reading	Class	Slides	Assignment	R
4.1 Panel Data and Fixed Effects Models
4.2 Difference-in-Difference Models
Problem Set 6 (Ungraded)
Final Exam 11:30AM Thurs December 12
Empirical Research Paper Project Due December 17
End of Semester/Advanced Regression	Reading	Class	Slides	Assignment	R
5.1 Instrumental Variables Models
5.2 Regression Discontinuity Models
5.3 Binary Dependent Variables Models
5.4 Machine Learning