Syllabus

ECON 031: INTRODUCTION TO ECONOMETRICS

Professor: Aleksandr Michuda
Email: amichud1@swarthmore.edu
Office: Kohlberg 220
Office Hours: MW 12-1 and by appointment
Meet with me: https://calendar.app.google/JnBAUe2jEY9fnMpQ7
Class Meeting and Location: MW 10:30-11:45; Kohlberg 115

Course Description and Goals

This course is an introduction to the field of econometrics, with a focus on the fundamental principles and techniques of descriptive and inferential statistics. The course emphasizes economic applications of statistical methods, particularly simple and multiple regression models. Students will learn about how to reason through data and the differences between correlation and causation. The only prerequisite for this course is Econ 1.

Upon successful completion of this course, students are expected to:

  • Understand basic statistical concepts and methods;
  • Apply basic statistical methods to simple economic applications;
  • Obtain data and conduct basic statistical analysis, using statistical software;
  • Present and interpret data and statistical analysis;
  • Critique simple statistical applications in economics.

Teaching Assistants

The TAs for the course are:

Sydney Ross <sross3@swarthmore.edu>

Bori Chung <bchung1@swarthmore.edu>

Grace Liu <gliu3@swarthmore.edu>

Nicholas Zuk <nzuk1@swarthmore.edu>

The TAs for this course will hold clinics on

  • Thursdays, 7-9pm Kohlberg 116

Required Materials

  • David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, & James J. Cochran. 2018. Essentials of Statistics for Business and Economics, 9th edition, Cengage Learning. (ASW).
    • The textbook will be used for problem set questions. If you get an earlier edition, the questions may (almost certainly) differ.
  • A non-graphing scientific calculator, e.g., TI-30 or similar. (You must have one for exams.)

Software

Stata is the preferred statistical software package to use for the course. It is available from the College’s software server. Some useful online resources for learning Stata include:

STATA Resource on Campus

Dr. Tao Wang (twang1@swarthmore.edu) is the Lead Associate of the Social Sciences Quantitative Lab. He is available through his own office hours as a resource for your Stata needs and your empirical projects. Ella Foster-Molina and the SSQL TAs will also hold office hours that you may attend; these hours are also described at the link above.

Dr. Wang will be teaching five Stata workshops during the course of our semester. You are required to attend the first workshop. You should have received an email from him about scheduling them. If you have not, please contact him ASAP to sign up. Please try to install Stata on your machine by January 26th. There will be time during the first workshop to help you install it if you run into trouble. The first two workshops should get you set up with Stata and comfortable using it for basic data analysis. Since problems sets will include Stata exercises, it is important that you attend these workshops.

Grading

Course grades will be based on one midterm (20 percent of your course grade), a non-cumulative final exam (20 percent of your course grade), problem sets (15 percent of your course grade), in-class group activities (25 percent of your course grade), and your semester-long group project (20 percent of your course grade).

Problem Sets

Problem sets will be assigned approximately every other week and will be due on Fridays. These will involve textbook problems, my own problems, and Stata exercises. You should start these early; they are not short. During the weeks when there is no problem set due on Friday, please stay engaged with Moodle, as there may be activities or readings that will help solidify your understanding of the material.

Group In-Class Activities

Every Wednesday, we will have group activities in class. These activities will include empirical exercises that will help hone your Stata skills and your understanding of econometric concepts. These exercises will be due at the end of the day on the day of the exercise. Oftentimes, they will require running data analysis, so bring your laptop. We will go over the answers on the following Monday. You will work in groups of 2-3 people for these activities and will be graded as a group. You can bring paper and write the answers, or submit them on Moodle. Your two lowest scores on these activities will be dropped.

Group Replication Project

The replication crisis in social science has led to changes in peer-reviewed journal policy that increases open access, reproducibility checks for code and transparent data. You will break up into groups of 2-3 people and choose a published, peer-reviewed paper that you are interested in and try to replicate the results in the paper. You will then submit a video with a presentation or some other way of describing your findings. Your project will be graded on the following criteria:

  1. How well you replicate the results (as a function of how easily replicable the paper was)
    • If you choose a paper that is easy to replicate, then you need to make sure that everything replicates perfectly. But oftentimes, this is not the case. So if the replication package was difficult to implement for whatever reason (difficult to understand code, missing data, missing output), you should highlight this in your presentation, and explain why replication wasn’t possible.
  2. A breakdown of issues you faced, how you overcame them or what you did to get as close as possible to replication
  3. Any extensions to the original paper
    • Replicating is hard enough, but the true test of a researcher is to push beyond that and think of an interesting extension.
    • This need not be a completely new and innovative idea. But it should be something that shows me that you understood the paper and thought critically about it.
  4. Topics and thoughts about future research
    • Did you end up still being interested in this paper?
    • Would you want to continue on this line of research?
    • What are some questions you would want to explore in future research?

Start thinking about potential papers early!

Policies

Make-up exams will not be given unless you provide documented evidence of a circumstance that merits rescheduling (e.g., illness). If the conflict is known beforehand, you must make arrangements with me well before the exam date. Late problem sets will only be accepted with permission from me, will suffer a grade penalty, and will not be accepted once the solutions have been posted on Moodle. The problem set on which you receive the lowest score (including a zero) will be dropped.

If you believe you need accommodations for a disability or a chronic medical condition, please visit the Student Disability Services website for details about the accommodations process. Since accommodations require early planning and are not retroactive, contact Student Disability Services as soon as possible. You are also welcome to contact me privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services.

Within this class, you are welcome to use foundation models in a totally unrestricted fashion, for any purpose, at no penalty. However, you should note that all large language models still have a tendency to make up incorrect facts and fake citations, or do math wrong. You will be responsible for any inaccurate, biased, offensive, or otherwise unethical content you submit, regardless of whether it originally comes from you or a foundation model. If you use foundation models or LLMs, its contribution must be acknowledged; you will be penalized for using it without acknowledgment.

The university’s policy on plagiarism still applies to any improperly cited use of work by other human beings, or submission of work by other human beings as your own.

Tentative Schedule

(Note: Read all sections of a chapter unless noted.)

Week Dates Topics Readings
1 Jan 21

Introduction; Describing Data; Install STATA on your computer

STATA Workshop (Jan. 22, 23)

 ASW 1
2 Jan. 26, 28

Descriptive Statistics

In-class Exercise #1

 ASW 2; ASW 3; PS1 due (Fri, 11:59pm)
3 Feb 2,4

Introduction to Probability;

STATA Workshop (Feb. 5, 6)

In-class Exercise #2: First Attempt

 ASW 4; ASW 5
4 Feb 9,11

Random Variables and Discrete Probability Distributions

In-class Exercise #2 Do-over

ASW 5

PS2 due (Fri, 11:59pm)
5 Feb 16, 18

Continuous Probability Distributions

In-class Excercise #3

 ASW 6
6 Feb 23, 25

Continuous Probability Distributions; Sampling Distributions

In-class Exercise #4

 ASW 6, ASW 7
7 Mar 2,4

Estimators

Midterm

 ASW 8; Introduce Group Project
8 Mar 9,11 Spring Break
9 Mar 16, 18

Comparison of Means and Proportions; Hypothesis Testing

In-class Exercise #5

STATA Workshop (Mar 19, 20)

 ASW 9, ASW 10; CIM 2
10 Mar. 23,25

Regression 1

In-class Exercise #6

 ASW 14; PS3 due (Fri, 11:59pm)
11 Mar 30, Apr 1

Regression 2

In-class Exercise #7

STATA Workshop (Mar 31, Apr 1)

ASW 14.8-14.9 ASW 15.1-15.5, 15.7

Group Project Outline Due
12 Apr 6,8

Multiple Regression;

In-class Exercise #8

STATA Workshop (April 9, 10)

 CIM 3; CIM 4.1; PS4 due (Fri, 11:59pm)
13 Apr 13,15

Directed Acyclic Graphs; Potential Outcomes

In-class Exercise #9

STATA Workshop (April 16,17)

 Instructor’s notes; PS5 due (Fri, 11:59pm)
14 Apr 20,22

Problems with Regression: Quasi-Experiments; IV; Sample Selection

In-class Exercise #10

 Instructor’s notes
15 Apr. 27, 29 Wrap up; PRESENT GROUP PROJECTS PS6 due (Fri, 11:59pm)
16 TBD Final Exam
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