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Syllabus

ECON 184B, Econometrics, Spring 2022
Instructor: Prof. Xinde "James" Ji
Lecture: T/Tu | 3:30-4:50 pm; https://brandeis.zoom.us/j/92770065343
Recitation: Wed 6:30-8:20 pm; https://brandeis.zoom.us/j/92770065343

Email: xji@brandeis.edu

This is a tentative version of the syllabus, and is subject to change without prior notification.

Teaching/Course Assistants:
Ailu Yu, ailuyu@brandeis.edu;
Sasha Skarboviychuk, sashaskarb@brandeis.edu

Disclaimer: Email is the preferred communication mechanism, and my team promise to respond to your email within 24 hours (48 hours on weekends). Using alternative communication media (for example instant messaging) does not guarantee a faster response.

Teaching Modes

This class is taught online only. Synchronous class sessions will run on T/Tu 3:00-4:50 pm, US Eastern Time. Video recordings of lectures will be posted shortly after each class session. You are FREE to choose between attending synchronous sessions or watch video recordings asynchronously. Whichever your choice, I will ensure that your learning outcomes are equally and fairly evaluated.

That said, you are encouraged to attend synchronous sessions if you are able to do so. There are at least several reasons:

1. The learning process of this class will be based on a series of in-class discussions and exercises. You will be better equipped to work on these problems in a peer environment.
2. The same amount of time, effort, and critical thinking are expected for synchronous and asynchronous learning. You still need to invest heavily in this class in order to earn a successful grade with asynchronous learning.

Overview

Is your Brandeis education worth it?¹ How much impact does climate change have on agriculture², the economy³, or your PSAT score?⁴ What explains the sex ratio imbalance in East Asia⁵ and Southeastern Asia⁶? Fascinating questions as they are, we are not here to seek answers to these questions directly (you are welcomed to check out relevant courses to these questions). Instead, you will be learning the methods and techniques that allow you to answer these questions or any other empirical questions that fascinate you.

Econometrics is the backbone of economics as a social science discipline: it offers the scientific rigor just like experiments to physics, chemistry, and medical sciences. Albeit, the "experiments" are set out in the real world, and econometrics provides a toolbox to learn from those experiments. Equally importantly, econometrics provides a framework to think about empirical problems in a rigorous way - this credibility revolution has certainly spilled over to other fields: e.g. political science, public health, psychology, business, and marketing.

In this class, you will be primed with the theoretical and the applied aspects of econometrics. You will learn the intuitions and statistical constructs behind various econometric tools; you will gain hands-on experience conducting econometric analysis using modern technology; and you will be able to read, interpret, and formulate empirical studies using econometric tools.

Learning goals

1. Know the math (with paper and pencil)
   • Understand the statistical properties of econometric estimation (expectations, hypothesis testing, etc.)
   • Understand the motivation and assumptions of common econometric tools (e.g., linear regression, nonlinear regression, instrumental variables regression, panel models, etc.)
2. Learn to code (with R)
   • Import, process, and manipulate data
   • Implement common econometric tools and interpret software outputs
   • Perform Monte Carlo simluations to evaluate properties of econometric tools
3. Explain the world (with a critical mind)
   • Critically evaluate empirical studies using econometric and statistical tools
   • Frame real-world problems into appropriate econometric models and provide meaningful answers

Prerequisites

Prerequisites: Econ 80a and Econ 83a; Econ 80a may be taken concurrently.

Textbooks

Required textbook: James H. Stock and Mark W. Watson, Introduction to Econometrics (4th edition), Pearson/Addison Wesley, 2019.

• You can use the 3rd edition as well.

If you are having difficulty purchasing course materials, please make an appointment with your Student Financial Services or Academic Services advisor to discuss possible funding options and/or textbook alternatives.

Readings
Additional readings will be posted on LATTE as the class goes along.

Grading

• In-class exercises: 12.5%
  • We will be solving most in-class exercises together. Gradings are based on semi-effort: you should be able to figure out the correct answers to these problems by the end of class.
• Problem sets: 20%
  • All problem sets are individual. There will be ~7 problems sets throughout the semester.
  • Only electronic submission is accepted. Late submissions will be accepted with a 10% penalty each day.
  • You need to submit both the compiled pdf/html file and the source code. You will receive a 10% bonus if your code is entirely reproducible with minimal altercation (e.g., package installation).
• Midterm 1: 25%*
  • Time TBD
  • Open book, open notes.
• Midterm 2: 25%*
  • Time TBD
  • Semi-cumulative (you will need to use knowledge from the first half of the course). Open book, open notes.
• Empirical project assignment: 17.5%
  • Instructions to follow.

Class Policies

Attendance

Synchronous participation is strongly recommended, and please keep your video on during the course of the synchronous lecture. If you choose to turn off your video, you are expected to answer my question when I call on you.

Recitation

Recitation is strongly encouraged but not mandatory. No graded parts will be assigned during recitation time. That said, recitation is an important component of the class where we commit dedicated times for you to strengthen your grasp on R coding, problem sets, and prepare for exams.

Computer and Equipments

We will be using interactive Zoom for lectures and office hours. All other class-related materials will be posted on LATTE. You will need to have the following software installed on your computer. These softwares are available across platforms (Windows, Mac, Linux) and free of charge.

• R (separate from RStudio)
• Rstudio

If you encounter any difficulties with pieces of equipment that hinder your learning, please reach out to me immediately, and I will do my best to help. Additionally, Brandeis offers student emergency funds to students in need. Email emergencyfunds@brandeis.edu for more information.

Credit Hours

Success in this four-credit course is based on the expectation that students will spend a minimum of nine hours of study time per week in preparation for class (readings, papers, discussion sections, preparation for exams, etc.

Academic Honesty

Every member of the University community is expected to maintain the highest standards of academic integrity. A student shall not submit work that is falsified or is not the result of the student's own effort. Infringement of academic honesty by a student subjects that student to serious penalties, which may include failure on the assignment, failure in the course, suspension from the University or other sanctions (see section 20 of Brandeis University Rights and Responsibilities). Please consult Brandeis University Rights and Responsibilities (see https://www.brandeis.edu/studentlife/srcs/rightsresponsibilities/index.html) for all policies and procedures related to academic integrity. Students may be required to submit work to TurnItIn.com software to verify originality. A student who is in a course or assignment should consult the faculty member responsible for that course or assignment before submitting the work. Allegations of alleged academic dishonesty will be forwarded to the Department of Student Rights and Community Standards. Citation and research assistance can be found at Brandeis Library Guides - Citing Sources (https://guides.library.brandeis.edu/c.php?g=301723).

Accommodations

Brandeis seeks to welcome and include all students. If you are a student who needs accommodations as outlined in an accommodations letter, please reach out me and present your letter of accommodation as soon as you can. I want to support you.

In order to provide test accommodations, I need the letter more than 48 hours in advance. I want to provide your accommodations but cannot do so retroactively. If you have questions about documenting a disability or requesting accommodations, please contact Student Accessibility Support (SAS) at 781.736.3470 or access@brandeis.edu.

Course Outline

Note: the course outline is alive and breathing, so it may evolve spontaneously as the course goes along.

1. Review of Probability and Statistics (Chapters 2 and 3)
2. Bivariate Regression (Chapters 4 and 5)
3. Multivariate Regression (Chapter 6)
4. Hypothesis Testing in Multiple Regression (Chapter 7)
5. Nonlinear Regression (Chapter 8)

-------Midterm 1 about here-------

6. Internal and External Validity (Chapter 9)
7. Panel Data (Chapter 10)
8. Limited Dependent Variable (Chapter 11)
9. Instrumental Variables (Chapter 12)
10. Experiments and Quasi-experiments (Chapter 13)

-------Midterm 2 about here-------

11. Big data (Chapter 14)
12. From 184 to Research

-------Empirical Project Due------

Footnotes

1. There has been extensive research on the return to education on earnings and non-wage outcomes. See, e.g., Card (1999 Handbook of Labor Economics) and Oreopoulos and Salvanes (2011 JEP). ↩

2. Deschenes and Greenstone (2007 AER): the Economics Impacts of Climate Change. ↩

3. Dell, Jones and Olken (2012 AEJ): Temperature Shocks and Economic Growth. ↩

4. Goodman et al. (2019 AEJ) Heat and Learning. ↩

5. Ebenstein (2010 JHR): the "Missing Girls" of China and the Unintended Consequences of the One Child Policy. ↩

6. Jayachandran and Pande (2017 AER): Why Are Indian Children So Short? Anukruti, Bhalotra and Tam (2021 EJ): On the Quantity and Quality of Girls. ↩