RES-ECON 312: Introduction to Econometrics

Example syllabus only – exact content subject to change. Please see your instructor’s syllabus for the current term for your specific course’s guidelines

Instructor: Dr. Emily Wang
@email

Lecture Information:
Time: Tuesdays/Thursdays 10:00 – 11:15
Location: Holdsworth Hall 202

Office Hours:
Time: Fridays 12:30 – 2:00 or by Appointment
Location: Stockbridge Hall 223

TA: Mehak Kaushik
@email

Office Hours:
Time: Wednesdays 2:00 – 4:00
Location: Zoom (Please use Canvas for Link, Located on Homepage)

Course Description

This course provides students with the foundational tools to analyze and interpret economic data, emphasizing both theoretical understanding and practical application. Students will gain proficiency in the statistical software R, learning to manage, visualize, and model data effectively.

Key econometric concepts such as causality, multicollinearity, bias, and hypothesis testing will be introduced, with a focus on conducting and interpreting multiple regression analyses using real-world data. By the end of the course, students will have developed valuable econometric skills and a deeper understanding of empirical methods that are widely applicable across academic, business, and policy- oriented settings.

These skills are not only essential for economic analysis but also highly marketable, making them an important addition to any resume. Whether pursuing graduate studies or entering the workforce, students will leave this course equipped to critically evaluate and produce data-driven analyses.

Goals & Objectives

My goal is to guide you as you develop valuable econometric skills and a deeper understanding of empirical methods. Many of our graduates have gone on to apply econometrics in business and government to estimate or predict impacts of changes. Our alums use the tools they learned to estimate how advertising and shelf-position in stores affects sales, the market potential of new drugs, enrollment rates at academic institutions, how costs affect firm pricing decisions, and the effect of EPA polices on consumer welfare to name a few applications. More broadly, I want you to develop key critical thinking skills that all employers seek in their applicants. When you read reports as part of your first job, you will understand what analysts did to generate their results and you will be prepared to think critically about the methods they used, whether they were appropriate, and what those results mean.

Below is a list of learning outcomes for this course.

Core Concepts:

• Understanding how econometrics bridges economic theory and real-world data.
• Distinguishing correlation from causality in regression analysis.
• Understanding endogeneity, multicollinearity, omitted variable bias, and interpretation of partial effects.

Empirical Tools and Techniques:

• Carrying out Multiple Regression Analysis using the statistical software R.
• Conducting hypothesis testing, model diagnostics, and explaining statistical output into meaningful economic insights.

Critical Thinking:

• Being able to write clearly and concisely about data analysis utilizing regression techniques to establish causal relationships among strategic variables and outcomes.

Class Material

Required:

• Textbook: Introductory Econometrics, by Jeffrey Wooldridge (Edition 8).
• Computer: Applying statistics or econometrics requires the use of computers. You are expected to be proficient with Word and Excel, which are used regularly for exercises and assignments. We will learn R, widely used in private industry and government workplaces.

Recommended:

I recommend several textbooks for your use. Lecture slides with the material you need to master will be provided for each lecture.

• Website: Introduction to Econometrics with R, by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
• Website: Using R for Introductory Econometrics, by Florian Heiss
• Textbook: Introduction to Econometrics, by James Stock & Mark Watson
• Textbook: Predictive Analytics for Business Strategy, by Jeffery Prince

Grades

Grades will be determined by the weekly problem sets and lab submissions, three exams, and one final project

Problem Set (30% of total grade)

We will have roughly 9 weekly problem sets. Each is composed of two parts: a theory exercise part and an R exercise part. Out of these, I will drop one lowest homework grades. Late homework will be assessed 5 penalty points per day (including weekend days) out of a total of 25 points (i.e. if you turn your homework 5 days late, you will not receive credit for it).

R Lab Submission (5% of total grade)

Each lecture will be divided into two parts. At the beginning of each lecture, we will cover a new theory topic. It will be followed by an R-Lab with exercises on the topic. The goal is to help you become familiar with R as well as getting hands-on experience with the concepts we cover in class. R-Lab submissions will be due at the end of the day. You are of course welcome to submit them during the lab. Out of these, I will drop the lowest three grades.

Exams (40% of total grade)

There will be two midterm exams and an optional final. The exams will be weighted in your favor, such that the best exam is weighted at 60% and second best is weighted at 40%. Midterm exams will be held in our classroom during class time. The final time and location are determined by the university. You will be required to apply theory and methods from lectures, possibly use software to estimate a model, complete analyses, and interpret the results. Tentatively midterm exams will be held on the following dates in class:

• Midterm 1: Thursday (March 26)
• Midterm 2: Thursday (May 7)

Please note that these dates are subject to change, as snow days and other schedule changes happen during the semester. Please check on Canvas for the most up-to-date schedule and exam dates.

Project (25% of total grade)

Form a team of two or three and think about a project for the semester. You and your teammates will complete a research paper using the econometric methods we learn in the course on a topic you are interested in. The project will be completed in three stages:

• Project Proposal: Due Mar 5 (5% of final project grade).
• Rough Draft: Due Apr 7 (15% of final project grade)
• Final Draft: Due May 5 (80% of final project grade)

Summarizing your Grade Calculation

Final grades will be calculated according to the following minimum cutoff points:

A = 93, A- = 90, B+ = 87, B = 83 B- = 80, C+ = 77, C = 73, C- = 70, D+ = 67, D = 60 and F<60

Online Material

All class materials will be posted on Canvas.

IMPORTANT: Canvas is where you should ALWAYS go to learn about announcements, upcoming exams, cases and anything important about the class. Please check Canvas frequently.

Communications Policy

You're always welcome to email me anytime! Whether you have a quick question, need help thinking through a concept, or just want to check in, feel free to reach out. I’ll do my best to get back to you within 36 hours, and usually it’s much sooner. If it’s something time-sensitive, just let me know in the subject line so I can prioritize it.

Tentative Outline of Course Topics

Relevant University and Departmental Policies Statements

Inclusive Learning & Academic Alerts

I have partnered with Student Success and your academic advisors to assist you on your path to success. Throughout the semester, I will communicate with Student Success & academic advisors regarding your progress in the course. If you are contacted, please consider scheduling appointments such as tutoring or academic advising. Referrals are not punitive and are meant to assist you in connecting with resources at UMass. Please email @email if you have any questions or need assistance connecting with resources.

Use of Artificial Intelligence

While you are welcome to discuss assignments with your classmates, I expect your reasoning and writing to be your own. This includes the use of computer-assisted content (AI, e.g. chatGPT). You may be AI in its editorial capacities, but anything beyond will be considered as cheating. Cheating on any work or exam will be pursued under the Code of Conduct procedures outlined by the university.

Academic Honesty Statement

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (http://www.umass.edu/dean_students/codeofconduct/acadhonesty/).

Accommodation Statement

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements. For further information, please visit Disability Services (https://www.umass.edu/disability/)

Title IX Statement (Responsible Employee)

In accordance with Title IX of the Education Amendments of 1972 that prohibits gender-based discrimination in educational settings that receive federal funds, the University of Massachusetts Amherst is committed to providing a safe learning environment for all students, free from all forms of discrimination, including sexual assault, sexual harassment, domestic violence, dating violence, stalking, and retaliation. This includes interactions in person or online through digital platforms and social media. Title IX also protects against discrimination on the basis of pregnancy, childbirth, false pregnancy, miscarriage, abortion, or related conditions, including recovery. There are resources here on campus to support you. A summary of the available Title IX resources (confidential and non-confidential) can be found at the following link: https://www.umass.edu/titleix/resources. You do not need to make a formal report to access them. If you need immediate support, you are not alone. Free and confidential support is available 24 hours a day / 7 days a week / 365 days a year at the SASA Hotline 413-545-0800.

For purposes of Title IX reporting, I am a considered a “responsible employee” at UMass (https://www.umass.edu/titleix/about). That means that if you tell me about a situation involving sexual assault, sexual harassment, domestic violence, dating violence, stalking, and retaliation, I must share that information with the Title IX Coordinator. Making a report to the Title IX Coordinator is my legal obligation, meets the University's goal of providing members of our community with supportive resources they might need, and enables the University to obtain a more accurate picture of the extent of sexual violence in our community. It will be completely up to you to determine if and how you want to work with the Title IX Coordinator's office. You will not be in trouble for reporting to me that you have experienced any of these situations, and the law prohibits retaliation against anyone who participates in a Title IX process.