ECONOMICS ECO 321.2 Introductory Econometrics

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Introductory Econometrics

ECONOMICS ECO 321.2

Department of Economics

General Information:

Prerequisite Note: Familiarity with mathematics and statistics is an essential prerequisite. Basic knowledge of calculus (especially simple partial derivatives), probability theory, and statistics are assumed throughout the course.

Prerequisite(s): C or higher in ECO 320 or AMS 310; C or higher in ECO 108.

You are responsible for ensuring that you have successfully completed all course prerequisites, and that you have not taken any anti-requisite courses. Lack of prerequisites may not be used as a basis for appeal. If you are found to be ineligible for a course, you may be removed from it at any time and you will receive no adjustment to your fees. This decision cannot be appealed.

If you find that you do not have the course prerequisites, it is in your best interest to drop the course well before the end of the add/drop period. Your prompt attention to this matter will not only help protect your academic record but will ensure that spaces become available for students who require the course in question for graduation.

Course Description:

An introductory course in regression analysis which covers: simple linear regression models and properties; hypothesis testing, multiple linear regression model; model specification; heteroskedasticity; endogeneity and instrumental variables; introduction to time series; and autocorrelation.

Course Objectives:

This course offers an introduction to basic linear regression methods that are heavily used in economics, business, and other data sciences. Linear regression is a primary tool to model and understand the relationships between variables given a sample of observations (or dataset).

By the end of the course, students should be familiar with simple linear regression, multivariate regression, testing hypotheses and conducting inference on these models, testing for violation in model assumptions and how to address. Students will also understand the basics of time series regression analysis and autocorrelation.

The empirical assignments will give students the opportunity to apply their knowledge to actual economic examples and applications. For instance, students will learn to apply the methods and analysis on real data using excel.

Course Learning Outcomes:

After successful completion of this course, students will be able to:

- Understand the fundamental concepts of econometrics and interpret regression results.

- Distinguish between correlation and causality

- Explain the concepts of unbiased and efficient estimators

- Analyze the properties of the simple and multiple linear regression model.

- Perform valid statistical inference

- Learn introductory analysis skills in excel

Textbook(s) and Course Materials:

Required

• Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 7th edition,

Cengage, 2016.

Introductory Econometrics A Modern Approach, 7th Edition, 7th Edition -

9781337558860 – Cengage

Alternate texts that will also serve:

- Earlier versions may have a similar presentation of the material, but individual

chapters may be rearranged.

- James H. Stock and Mark W. Watson, Introduction to Econometrics, 4th Edition

ISBN-13: 9780134461991.

- Dougherty, Christopher. Introduction to econometrics. Oxford university press, USA,

2011.

Required Software: R (Notes on installation and use are available on the course website)

Organization: Class meetings will consist of lecture, discussions, and applications. All students are expected to have read the assigned material before class. The discussions are designed to convey the underlying theory of an econometric topic, while the applications are designed to further understanding and get the student comfortable with using the R software. It is important to pay attention and follow along with the applications to ensure your software is fully functional and you can complete relevant assignments. Be sure to ask questions if the subject at hand is not becoming clear.

Exams:

• 2 Midterm: TBA (during class time)

• Final: TBA

You must take the final exam at your scheduled section time. Make your travel plans around these dates. There will be no early exams given.

Assignments: There will be several assignments throughout the course. They will be a mix of analytical and empirical problems and will be assigned and submitted via the course website. All due dates will be explicitly given, and answer keys will be made available immediately after. No late assignments will be accepted.

Course Project: A research project will require the student to apply an econometric analysis to a topic of the student’s own choice. The grade for the project will primarily reflect the student’s command of econometrics as evidenced by the work. The project will be due in stages throughout the term, and it is important that each stage is completed in a timely manner. In particular, we will be using your project data to complete relevant assignments. Not having data means you cannot get full credit on assignments.

• Project Proposal due: TBA (Submission counts as an assignment) 2 weeks after the first exam.

• Data Collection / Progress Report due: TBA (Submission counts as an assignment) 4 weeks after the first exam

• Final Project Report due: TBA (No late submissions will be accepted) week before the last exam.

Grading:

• Assignments: 20%

• Research Project: 15%

• 2 Midterm Exam: 40%

• Final Exam: 20%

•Attendance: 5%

(Tentative) Course Schedule:

The possibility exists that unforeseen events will make schedule changes necessary. Any changes will be clearly noted in course Announcements and/or through Stony Brook email.

Week	chapter
1	Intro: syllabus, course tools
	What is Econometrics and introduction to R
2	Simple Regression Mode
3	Simple Regression Mode
	Assig 1 due
4	Exam 1
	Intro to the project
5	Multiple Regression Analysis
6	Multiple Regression Analysis
	Assig 2 due
7	Gauss Markov and Hypothesis testing
8	Gauss Markov and Hypothesis testing
	Assig 3 due
9	Exam 2
10	Dummy and Categorical Variables
11	Dummy and Categorical Variables
	Assig 4 due
12	Heteroskedasticity
13	Heteroskedasticity
	Assig 5 du
14	Endogeneity
14	Endogeneity
	Assig 6 due
15	Time Series
15	Time Series
	Final

University and Course Policies

Student Accessibility Support Center Statement:

If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact the Student Accessibility Support Center, Stony Brook Inion Suite 107, (631) 632-6748, or at [email protected]. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential.

Students who require assistance during emergency evacuation are encouraged to discuss their needs with their professors and the Student Accessibility Support Center. For procedures and information go to the following website: https://ehs.stonybrook.edu/programs/firesafety/emergency-evacuation/evacuation-guide-people-physical-disabilities and search Fire Safety and Evacuation and Disabilities.

To receive an accommodation for a covid related absence please see:

https://www.stonybrook.edu/commcms/studentaffairs/studentsupport/Covid%20Support.php

Academic Integrity Statement:

Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty is required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Technology & Management, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at http://www.stonybrook.edu/commcms/academic_integrity/index.html

Important Note: Any form of academic dishonesty, including cheating and plagiarism, will be reported to the Academic Judiciary.

Critical Incident Management:

Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of University Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures. Further information about most academic matters can be found in the Undergraduate Bulletin, the Undergraduate Class Schedule, and the Faculty-Employee Handbook.

Course Policies:

Understand When You May Drop This Course:

If you need to drop or withdraw from the course, it is your responsibility to be aware of the tuition liability deadlines listed on the registrar’s Academic Calendar. Before making the decision to drop/withdraw you may want to [contact me or] refer to the University’s policies:

● Undergraduate Course Load and Course Withdrawal Policy

● Graduate Course Changes Policy

Incomplete Policy:

Under emergency/special circumstances, students may petition for an incomplete grade. Circumstances must be documented and significant enough to merit an incomplete. If you need to request an incomplete for this course, contact me for approval as far in advance as possible. You should also read the University’s policies that apply to you:

Undergraduate Bulletin

Graduate Bulletin

Course Materials and Copyright Statement:

Course material accessed from Blackboard, Zoom, Echo 360, VoiceThread, etc. is for the exclusive use of students who are currently enrolled in the course. Content from these systems cannot be reused or distributed without written permission of the instructor and/or the copyright holder. Duplication of materials protected by copyright, without permission of the copyright holder is a violation of the Federal copyright law, as well as a violation of Stony Brook’s Academic Integrity.