Course Syllabus

Introduction to Applied Econometrics

Econ 400 (4 Credits), Fall 2018

University of Wisconsin-Madison

Instructor: Christopher McKelvey

E-mail: cmckelvey@wisc.edu

Course Webpage

Lecture notes and other course materials will be posted on Canvas: http://canvas.wisc.edu/

Course Overview

This course provides an introduction to econometrics –  the statistical methods economists use to evaluate empirical relationships and test economic theory. Because this is an applied course, our emphasis will be on (a) developing the econometric skills necessary to read and understand empirical papers in economics and (b) the application of econometrics to real-world data using modern statistical software. A wide range of statistical techniques will be covered, including: univariate & multiple regression, differences-in-differences, instrumental variables, limited dependent variables, fixed-effects models, and regression discontinuity. Throughout the course, we will be interested in the extent to which statistical models can be used to conduct causal inference.

Note: Our department offers two introductory econometrics courses: Econ 400 and Econ 410. Econ 400 places less emphasis on theory and a correspondingly greater emphasis on applied techniques. Econ 410 takes a more mathematical & theoretical approach, deriving formulas and proving results wherever possible. Econ 400 and 410 are not a sequence. Students take only one of the two. Students doing our math emphasis major, including all those pursuing honors in the major, must take Econ 410. 

Prerequisites

Prerequisites for this class are Econ 310 and either Math 211 or 221.

Class Meetings and Office Hours

Lectures are Tuesdays and Thursdays from 2:30 - 3:45 p.m. in Social Science 5206.

My office hours will be Mondays from 2:30 to 4:00 p.m. in Social Science 7321. Your TA will announce his or her office hours at your first discussion section.

Textbook and Required Materials

Our textbook is: James H. Stock and Mark W. Watson (2014). Introduction to Econometrics. Pearson, Updated Third Edition (ISBN: 9780133486872).

If you prefer to save a bit of money, you should also be able to get by using the original Third Edition (ISBN: 9780138009007) or the International Softcover Edition (ISBN: 9789352863501).

We will not be using any of Pearson’s online resources, so there is no need to pay extra for a textbook that comes bundled with MyLab.

While completing the problem sets, midterm, and final exam, you will need a calculator with the following functions: x^y and e^x.. 

Evaluation

Your overall grade for the course will be based on three components:

• Midterm Exam: The midterm for this class is worth one third of your overall grade and will be held October 25. Ensure you are available this day, as the midterm will not be rescheduled for any student for any reason. On a case by case basis, in the event of a truly unavoidable absence (submit a written statement in advance of the exam), I may elect to shift the weight of a missed midterm to the final exam. Note, however, that a cold or flu does not constitute an unavoidable absence –  illnesses must be severe enough to make attendance impossible and must be fully documented.
• Final Exam: The (cumulative) final for this class is worth one third of your overall grade and will be held 10:05am - 12:05pm on December 18. As with the midterms, in a class this size it is not possible to reschedule the final – even when students have multiple exams in a 24 hour period. However, if you have another exam at exactly the same time, then I am willing to reschedule so long as you provide evidence of enrollment in a class with a conflicting final and notify me at least two weeks in advance.
• Problem sets: There will be weekly problem sets, which together are worth one third of your overall grade – so completing them will be critical to your success. For full credit, problem sets must be submitted in Canvas before the submission deadline. Late problem sets may be submitted after this deadline, but will receive a 20% per day deduction. In order to provide you with hands-on experience using the methods taught in this course, the computer package Stata will be used extensively on the problem sets. To receive full credit, you must submit your Stata log. To help prepare you for these assignments, Stata will be used during lectures and tutorials will also be provided during discussion section. You are encouraged to form a study group with your classmates, but you must write up your answers independently (meaning that you should not be looking at another student’s answers as you write up your own). Problem sets with identical answers will not be accepted (i.e., receive zero credit).
  

Your overall grade for this class will be curved. This curve can help your grade, but cannot hurt it. I achieve this by computing your grade using two different methods. First, I assign grades according to a percentage scale, where A = [92,100], AB = [88,92), B = [82,88), BC = [78,82), C = [70,78), D = [60,70), F = [0,60). (In other words, if you receive a grade in the class of 92% or better, then you’ll receive an A.) Second, I assign grades according to a percentile scale, where A = [83,100], AB = [65,83), B = [45,65), BC = [25,45), C = [6,25), D = [3,6), F = [0,3). (In other words, if you perform better than 83% of the class, then you’ll receive an A). Your overall grade in the class is the higher of these two grades.

I strive to make all of the grading transparent and fair. If you are unhappy with the way a problem has been graded, I encourage you to discuss it with me, but you must bring the concern to me within two weeks of when you were first able to view the graded problem set or exam.

Learning Outcomes

Following the completion of this course, students will be able to:

• Discuss the properties of an ordinary least squares (OLS) estimator for a linear 
regression model
• Test theories about the true model using formal hypothesis tests
• State the assumptions underpinning OLS, recognize violations of these assumptions, discuss the consequences of such violations, and – where possible – suggest alternative statistical approaches that are more appropriate given the circumstances
• Evaluate the extent to which econometric methods can be used to determine whether a statistical association represents a causal relationship
• Use statistical software to apply these statistical techniques to analyze the relationship between real-world economic variables
• Read and interpret results from applied economics journal articles that employ these statistical techniques

Credits

This class meets for a total of 4 class period hours each week of the semester and carries the expectation that students will work on course learning activities (reading, writing, problem sets, studying, etc) for about 2 hours out of classroom for every class period. This syllabus includes additional information about meeting times and expectations for student work.

Students with Disabilities

If you have approval from the McBurney Center for disability-related accommodations, please contact me to discuss how these accommodations will be implemented for this course. This should be done as soon as possible, and no later than two days before the first midterm.

Grievance Procedure

The Department of Economics has developed a grievance procedure through which you may register comments or complaints about a course, an instructor, or a teaching assistant. The Department continues to provide a course evaluation each semester in every class. If you wish to make anonymous complaints to an instructor or teaching assistant, the appropriate vehicle is the course evaluation. If you have a disagreement with an instructor or a teaching assistant, we strongly encourage you to try to resolve the dispute with him or her directly. The grievance procedure is designed for situations where neither of these channels is appropriate.

If you wish to file a grievance, you should go to room 7238 Social Science and request a Course Comment Sheet. When completing the comment sheet, you will need to provide a detailed statement that describes what aspects of the course you find unsatisfactory. You will need to sign the sheet and provide your student identification number, your address, and a phone where you can be reached. The Department plans to investigate comments fully and will respond in writing to complaints.

Your name, address, phone number, and student ID number will not be revealed to the instructor or teaching assistant involved and will be treated as confidential. The Department needs this information, because it may become necessary for a commenting student to have a meeting with the department chair or a nominee to gather additional information. A name and address are necessary for providing a written response.

Misconduct Statement

Academic Integrity is critical to maintaining fair and knowledge based learning at UW Madison. Academic dishonesty is a serious violation: it undermines the bonds of trust and honesty between members of our academic community, degrades the value of your degree and defrauds those who may eventually depend upon your knowledge and integrity.

Examples of academic misconduct include, but are not limited to: cheating on an examination (copying from another student's paper, referring to materials on the exam other than those explicitly permitted, continuing to work on an exam after the time has expired, turning in an exam for regrading after making changes to the exam), copying the homework of someone else, submitting for credit work done by someone else, stealing examinations or course materials, tampering with the grade records or with another student's work, or knowingly and intentionally assisting another student in any of the above. Students are reminded that online sources, including anonymous or unattributed ones like Wikipedia, still need to be cited like any other source; and copying from any source without attribution is considered plagiarism.

The Dept. of Economics will deal with these offenses harshly following UWS14 procedures (http://students.wisc.edu/saja/misconduct/UWS14.html):

1. The penalty for misconduct in most cases will be removal from the course and a failing grade.
2. The department will inform the Dean of Students as required and additional sanctions may be applied.
3. The department will keep an internal record of misconduct incidents. This information will be made available to teaching faculty writing recommendation letters and to admission offices of the School of Business and Engineering.

If you think you see incidents of misconduct, you should tell your instructor about them, in which case they will take appropriate action and protect your identity. You could also choose to contact our administrator (Tammy Herbst-Koel: therbst@wisc.edu) and your identity will be kept confidential.