Course Syllabus

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Coady Wing

Office: SPEA 339A

Email: cwing@indiana.edu

Office Hours: Most of the time, you can simply drop by my office and I will be happy to talk right then, or you can make an appointment by email.

 

Jaehyun Choi (TA)

Email: jc234@iu.edu

Office: SPEA 334

Office Hours: Thursday afternoons and by appointment

 

Jennifer Mangano (TA)

Email: jenmanga@iu.edu

Office: SPEA 334

Office Hours: Thursday afternoons and by appointment

 

 

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FA26 SPEA V706 Syllabus.docx

 

Course Time

 

Lectures: Monday and Wednesday 12:45 - 2:00 pm SPEA PV 273

Lab Session: Monday 5:30 – 7:30 pm SPEA PV 275

 

Instructor Information

Coady Wing

SPEA 339A

cwing@iu.edu

Office Hours: by appointment

 

Teaching Assistants

 

Jaehyun Choi

SPEA 334

jc234@iu.edu

Office Hours: Thursday afternoons and by appointment

Jennifer Mangano

SPEA 334

jenmanga@iu.edu

Office Hours: Thursday afternoons and by appointment

 

 

Course Description (IU Bulletin)

Graduate-level introductory statistics. Focus is on estimation of model parameters using least square methods. Topics include properties of estimators, ordinary least squares, instrumental variables, two- and three-stage least squares, assumptions, consequences when assumptions are false, and alternate methods when assumptions are false. Emphasis on matrix representations and simulation methods.

 

Overview

This is the first course in the quantitative methods sequence for Ph.D. students at the O’Neill School. The course introduces foundational ideas in statistics and econometrics that are essential for applied empirical research in various academic fields. The main topic is the linear regression model. We examine the key assumptions of the model, the mechanics of least squares estimation, statistical inference and hypothesis testing, and instrumental variables estimation. The contents of this course will be theoretical and practical, including hands on work using R.

 

Learning Outcomes

By the end of the semester, students should be able to:

  • Describe the key assumptions involved in linear regression models, and explain how and why these assumptions affect the interpretation of standard estimators. In particular, students should be able to explain the role of assumptions related to heteroscedasticity, independence, and exogeneity. 
  • Interpret and critically analyze the results of a regression model applied to settings related to public affairs.
    • Discuss whether key assumptions are credible in a specific situation.
    • Discuss how alternative modeling techniques, data sources, and research designs could be used to strengthen a study if the key assumptions are in doubt. 
  • Clean and analyze data and estimate statistical models using R.
  • Develop and implement a data analysis plan for a research project that is related to public affairs.
    • Develop a statistical model that is relevant to a specific research question
    • Organize, combine, and transform data sources to facilitate research
    • Implement a data management system and workflow that helps ensure that results are well-documented, replicable, and secure. 
  • Communicate the essential elements of a statistical analysis in written and verbal form.

 

Course Materials

 

Online Resources

 

Textbooks

 

Course Organization: This course consists of twice weekly lectures and a weekly lab section. The course contains three exams, weekly assignments, and weekly quizzes that are described below. It is imperative that you attend all components of the class.

 

Exams: There will be 3 exams in this course: two midterm exams and a final exam. Midterms will be administered during the lab sessions. Each midterm will be worth 15% of your course grade. The final exam will be worth 20% of your course grade and take place at the date and time below. The exams provide some opportunity for exposure to questions that are related to the material on the PhD comprehensive exams.

 

Midterm Exam 1: Wednesday, September 30

Midterm Exam 2: Wednesday, November 4

Final Exam: Friday, December 18 from 12:40-2:40 pm

 

Weekly Assignments (Problem Sets): The course has weekly assignments (approximately 15 throughout the semester). The assignments will generally include a combination of two types of problems: data analysis problems that give you a chance to work with the course material in an applied setting using R, and conceptual problems that you will answer using some combination of mathematical analysis, social science theory, or reference to some underlying literature. The assignments are in part designed to ensure that students work with data and with the concepts covered in the course material on a regular basis. Late assignments will not be accepted and you will have about one week to work on each assignment. Your lowest assignment score will be dropped.

 

Lab Sessions: TA-facilitated lab sessions are an integral part of the course. The work you do in the lab supports several of the course objectives. It gives you a structured opportunity to work with data, and to implement the methods we discuss in class. They are group sessions, and they are meant to inspire discussions and conversations between students. This type of collaboration is important for understanding the material, and for learning how to participate in scholarly debates about statistics and econometrics.

 

Most of the lab sessions will focus on the assignment of the week. You will be able to work through the material using the TA and your classmates as resources. The TAs will play a role in developing and implementing the weekly assignments. And they will grade the assignments. Sometimes, one of the TAs will provide a tutorial on a particular topic or method. For example, early in the semester, they will provide an introduction to R. 

 

Lab Quizzes: At the beginning of each lab, there will be a quiz to check your understanding of the material. The quizzes will be done on Canvas under the lockdown browser or an equivalent restriction so that AI tools cannot be used. Quizzes may include both conceptual questions and data analysis tasks in R. Quizzes must be taken during the lab—if you do not attend lab, you will not be able to get these points. The lowest quiz score will be dropped. In total, these quizzes will be worth 30% of your grade. A portion of your grade on each quiz will be based on participation alone.

 

 

Percent of Total

Lab Quizzes

30%

Weekly Assignments

20%

Midterm Exam 1

15%

Midterm Exam 2

15%

Final Exam

20%

Total

100%

 

 

 

Tentative Schedule

 

Week

Topics

Reading

Assignments

Week 1

August 24 (M)

August 26 (W)

Introduction to the course and the subject of econometrics.

 

Conditional Expectation Functions

Hansen-Chapter 1

MHE-Chapter 1-2

Hansen-PSE Chapters 1 and 2.

(And Chapters 3,4,5 for reference)

Quiz #1 (Due 8/24)

Assignment #1 (Due 8/30)

Week 2

August 31 (M)

September 2 (W)

Conditional Expectation Functions

Hansen – Chapter 2

MHE – Chapter 3

Quiz #2 (Due 8/31)

Assignment #2 (Due 9/6)

Week 3

September 7 (M)

No Class – Labor Day

September 9 (W)

Algebra of Least Squares

Hansen – Chapter 3

MHE – Chapter 3

Assignment #3 (Due 9/13)

Week 4

September 14 (M)

September 16 (W)

Least Squares Regression

Hansen – Chapter 4

Quiz #3 (Due 9/14)

Assignment #4 (Due 9/20)

Week 5

September 21 (M)

September 23 (W)

Sampling and Estimation for Regression

Hansen – PSE Chapter 6

Hansen – Chapter 4

Quiz #4 (Due 9/21)

Assignment #5 (Due 9/27)

Week 6

September 28 (M)

September 30 (W)

Sampling and Estimation for Regression cont.

 

Midterm #1 (9/28)

Week 7

October 5 (M)

October 7 (W)

Large Sample Theory

Hansen – PSE Chapter 7 and 8

Hansen – Chapter 6

Hansen – Chapter 7

Quiz #5 (Due 10/5)

Assignment #6 (Due 10/11)

Week 8

October 12 (M)

October 14 (W)

Asymptotic Theory for Least Squares

Hansen Chapter 7

Quiz #6 (Due 10/12)

Assignment #7 (Due 10/18)

Week 9

October 19 (M)

October 21 (W)

Hypothesis Testing

Hansen – Chapter 8 and 9

Quiz #7 (Due 10/19)

Assignment #8 (Due 10/25)

Week 10

October 26 (M)

October 28 (W)

Resampling Methods and the Bootstrap

Hansen – Chapter 10

Quiz #8 (Due 10/26)

Assignment #9 (Due 11/1)

Week 11

November 2 (M)

November 4 (W)

Putting Things Together: Causal Inference, Omitted Variable Bias, and Regression

MHE – Chapter 1-3

Midterm #2 (11/2)

Week 12

November 9 (M)

November 11 (W)

Instrumental Variables

Hansen – Chapter 12

MHE – Chapter 4

Quiz #9 (Due 11/9)

Assignment #10 (Due 11/15)

Week 13

November 16 (M)

November 18 (W)

Two Stage Least Squares

 

Quiz #10 (Due 11/16)

Assignment #11 (Due 11/22)

Week 14

November 23-29

 

Thanksgiving Break

 

 

Week 15

November 30 (M)

December 2 (W)

Experiments and Quasi-Experiments with Instrumental Variables

Hansen Chapter 3

MHE Chapter 4

Quiz #11 (Due 11/30)

Assignment #12 (Due 12/6)

Week 16

December 7 (M)

December 9 (W)

Pit-falls of Instrumental Variables

Hansen Chapter 3

MHE Chapter 4

Quiz #12 (Due 12/7)

Assignment #13 (Due 12/13)

Finals Week

December 18

 

 

Final Exam (12:40-2:40 pm)

 

 

O’Neill School of Public and Environmental Affairs

Student Honor Code

Introduction

The O’Neill School takes matters of honesty and integrity seriously. We expect O’Neill students will, at all times, maintain academic integrity and civil and professional standards of conduct.

Scope

O’Neill’s Student Honor Code applies to all courses and programs offered by O’Neill, and to all students who participate in O’Neill classes and programs, regardless of whether students are admitted to O’Neill. Students studying at O’Neill are expected to continue their commitment to ethical behavior in all their endeavors, including, but not limited to, coursework inside and outside of O’Neill, internships, community service, and overseas study.

O’Neill’s Student Honor Code incorporates IU’s Code of Student Right, Responsibilities, & Conduct and extends to matters of civility and professional conduct, including, expectations about class attendance, punctuality, participation in class, and decorum in class and at other school functions.

Academic Conduct Requirements All students enrolled in O’Neill courses are subject to the academic misconduct provisions found in the IU Code of Student Rights, Responsibilities, & Conduct.

Academic misconduct is defined as any activity that tends to undermine the academic integrity of the institution. The IU Code of Student Rights, Responsibilities, & Conduct lists and defines many types of academic misconduct, including cheating, fabrication, plagiarism, interference, violation of course rules, and facilitating academic dishonesty.

Academic misconduct includes the use of any technology or assistance, including AI, that is not permitted by the instructor.

Civility and Professional Conduct Requirements

O’Neill requires that all students treat others with mutual respect and behave professionally in the classroom and during all academic and non-academic activities.

These civility and professional conduct requirements foster an appropriate atmosphere inside and outside the classroom:

  • Students must act in accordance with the O’Neill School mission and values.
  • Students must regularly attend and be fully prepared for class, including completing readings.
  • Students must not behave in a way that interrupts or distracts from teaching or learning. For example, students must be punctual in their arrival to class and be present and attentive for the duration of the class. They must not sleep, do work for another class, or wander in and out of the classroom, pack up, or leave class early (unless an emergency exists).
  • Students must abide by the course policy regarding use of electronic devices in the classroom, including a requirement that no electronics are allowed or that students are not permitted to use devices for non-class related purposes.
  • Students must responsibly participate in class activities and during team meetings for course assignments.
  • Students must attend scheduled meetings with faculty or staff, events for which they have RSVPed, interviews with prospective employers, and student organization meetings.
  • Students must address faculty members, other students, and others appropriately and with respect, whether in person, in writing, or in electronic communications.
  • Students must show tolerance and respect for others.
  • Students must practice respect and responsible stewardship of the physical resources of the school, refraining from destroying or defacing school property, integrating practices of sustainability, and contributing to a clean and orderly physical environment in all buildings, classrooms, offices, and meeting spaces.
  • Students must comply with the personal misconduct responsibilities within the IU Code of Student Rights, Responsibilities, & Conduct.

Reporting

Students must abide by the O’Neill Student Honor Code themselves and hold their peers to the same standards. Students should report suspected instances of academic, civility, or professional misconduct to the instructor of the course. Students should report suspected instances of personal misconduct to the IU Office of Student Conduct.

Course Requirements:

  1. List all assignments (exams, quizzes, papers, presentations, etc.) that will factor into students’ final grade for the course. Grading rubrics/criteria of some kind must accompany assignments but need not be posted here.
  2. List percentage values for each assignment, as well as attendance if it impacts the final grade.
  3. Grading scale. Use the Canvas grading scale unless you specify otherwise.
  4. Academic integrity. Instructors may wish to elaborate, but at a minimum must refer students to their responsibilities at https://studentcode.iu.edu/ and include the below information on note selling and materials ownership.

Counseling and Psychological Services

For information about services offered to students by CAPS: http://healthcenter.indiana.edu/counseling/index.shtml

TimelyCare

Indiana students have free, 24/7 access to virtual mental health care services with TimelyCare. Students do not need insurance to access TimelyCare services. https://www.iu.edu/mental-health/find-resources/timely-care.html#0

Religious Accommodation Requests

The "Request for Accommodation for Religious Observances" form must be submitted by the student to the professor no later than two weeks prior to the anticipated absence. A separate form must be submitted for each absence and for each course. Faculty do not have to consider accommodations for the purpose of allowing students to travel away from Bloomington for religious observance. Information about the policy is found here: https://vpfaa.indiana.edu/resources/religious-observances-information.html

Accessible Educational Services (formerly Disability Services for Students)

Securing accommodations for a student with disabilities is a responsibility shared by the student, the instructor, and the AES Office. For information about support services or accommodations available to students with disabilities, and for the procedures to be followed by students and instructors: https://studentlife.indiana.edu/care-advocacy/iub-aes/index.html

Sexual Harassment

As your instructor, one of my responsibilities is to help create a safe learning environment on our campus. Title IX and our own Sexual Misconduct policy prohibit sexual misconduct. If you have experienced sexual misconduct, or know someone who has, the University can help.

If you are seeking help and would like to talk to someone confidentially, you can make an appointment with:

  1. The Sexual Assault Crisis Service (SACS) at 812-855-8900
  2. Counseling and Psychological Services (CAPS) at 812-855-5711
  3. Confidential Victim Advocates (CVA) at 812-856-2469
  4. IU Health Center at 812-855-4011

For more information about available resources: http://stopsexualviolence.iu.edu/help/index.html. It is also important to know that federal regulations and University policy require me to promptly convey any information about potential sexual misconduct known to me to our campus’ Deputy Title IX Coordinator or IU’s Title IX Coordinator. In that event, they will work with a small number of others on campus to ensure that appropriate measures are taken and resources are made available to the student who may have been harmed. Protecting a student’s privacy is of utmost concern, and all involved will only share information with those that need to know to ensure the University can respond and assist. I encourage you to visit http://stopsexualviolence.iu.edu/help/index.html to learn more.

IU Culture Centers: https://studentlife.indiana.edu/communities/culture-centers/index.html

 

 

Course Summary:

Course Summary
Date Details Due