Syllabus

The key components of the syllabus are outlined below. You can access the complete syllabus in PDF format by clicking here.

Overview

This course covers the basics of conducting quantitative economic research. The course aims to take you through the steps involved in answering a research question using observational data. You will learn and implement statistical and econometric concepts vital to empirical research. You will select a question, locate data to answer it and use the tools we learn in this class to answer this question.

This course will involve hands-on work with data using R, a statistical software, both inside and outside the classroom. The tools learned in this class will be helpful regardless of whether your goal is to be a researcher, a consultant, run your own business, or work for a non-profit.

Learning Goals

Upon successful completion of this course, students will be able to discern valuable insights from datasets and communicate empirical findings effectively. In particular, you will

  • Develop a strong grasp of both the conceptual and practical aspects of various statistical and econometric tools.
  • Learn to tidy, wrangle, manipulate, and visualize data using TidyVerse in R.
  • Be able to compute descriptive statistics, perform regression analysis in R, and present results in a clear, elegant manner.
  • Gain the skill to effectively communicate empirical findings.
  • Develop an understanding of causality, including the ability to identify and articulate potential threats to causal inference
  • Get an introduction to advanced topics at the forefront of economic research, such as quasi-experimental methods and machine learning.

Course Structure

All meetings for this course are expected to take place in a physical classroom. During class, in addition to my delivering the day’s topics via lecture slides, you will often be expected to engage in collaborative problem-solving with handouts, work on R programming exercises, and participate in class discussions. The course is designed to be interactive, and requires you to actively participate. Therefore, regular class attendance is especially important for you to suceed in this class.

A significant portion of this class is focused on developing a research paper, which you can complete individually or with a classmate. You can either choose your own dataset or use one of the datasets that I have compiled for this class in the Econ340 Datasets Dropbox folder. You’ll begin the research project early in the semester, with assignments focused on formulating a question, selecting data, and conducting descriptive analysis. I’ll provide guidance and support throughout the semester and will schedule a mid-semester meeting to review your progress.

Course Material

All course materials—including lecture slides, handouts, notes for each topic—are available on the course website. These materials should generally be sufficient, and there is no mandatory textbook for this class. However, if you have a keen interest in the subject and seek additional references, the following options are great choices:

Grading Criteria

Your course grade will be determined according to the following breakdown:

Component Points
Active Engagement 10
Problem Sets 20
Research Paper: Interim Submissions 15
Research Paper: Final Submission 15
Midterm Exam 20
Final Exam 20
Date Lecture Module Topics Due
Tue 01/21 1 Describing Data Introductions; Summation notation
Thu 01/23 2 Distribution, mean, median, percentiles
Tue 01/28 3 Variance, standard deviation, Z-score
Thu 01/30 4 Covariance and correlation
Tue 02/04 5 Research questions and data Problem Set 1
Thu 02/06 6 Coding in R Getting started with R RP Team
Tue 02/11 7 Importing and cleaning data in R
Thu 02/13 8 Describing variables in R
Tue 02/18 9 Random Variables Distribution, expectation, variance Problem Set 2
Thu 02/20 10 Normal distribution, Z-score
Tue 02/25 11 Independence, correlation RP Submission 1
Thu 02/27 12 Sampling and Estimation Sample mean distribution; Good estimators
Tue 03/04 13 Confidence intervals
Thu 03/06 14 Hypothesis testing and p-values Problem Set 3
Tue 03/11 Review Class
Thu 03/13 Midterm Exam
Tue 03/18 Research Project Feedback
Thu 03/20 15 Linear Regression Ordinary least squares (OLS), Goodness of fit
Tue 03/25 16 Prediction vs. causal inference
Thu 03/27 17 Inference (p-values, t-stats, confidence intervals)
Spring Recess
Tue 04/08 18 Linear Regression (cont.) Omitted variable bias; Multiple regression model RP Submission 2
Thu 04/10 19 Categorical variables; Interaction terms
Tue 04/15 20 Quadratic and log functional forms
Thu 04/17 21 Recap and synthesis
Tue 04/22 22 Linear regression in R Problem Set 4
Thu 04/24 23 Linear regression in R
Tue 04/29 24 Advanced Topics Experiments and quasi-experimental methods
Thu 05/01 25 Panel data and event study designs
Tue 05/06 26 Big data and machine learning Final Paper
Thu 05/08 Review Class
Thu 05/15 Final Exam (1--2.50 pm)