E 350: B S

Spring 2016
TR 11am – 12:15pm
205 Bryan Building
INSTRUCTOR: Professor Dora Gicheva
Office: 459 Bryan Building
Phone: 334-4865
E-mail: d_gichev@uncg.edu
Office Hours: By appointment
COURSE DESCRIPTION: The purpose of this course is to provide you with a theoretical and
empirical foundation in econometrics. Econometrics is concerned with the application of
statistical techniques to the analysis and interpretation of economic phenomena. These
techniques are best learned (and retained) by not only reading about them but also by
applying them to real-world data. Accordingly, we will take a very “hands-on” approach to
the study of econometrics.
The course consists of two parts: lectures and labs. In the lectures, we will discuss
econometric theory and work through relevant exercises. In the computer labs, we will work
with actual data using the statistical software package SAS to make predictions and infer
causal relationships. This is an important skill that has a very high return in today’s
marketplace (resume booster!). Moreover, for those of you considering entering the M.A.
program in Economics, a program that makes heavy use of econometrics, this course will
help you determine if the M.A. program is right for you.
TEXTBOOK: The textbook we will use in this course is Using Econometrics, by A.H.
Studenmund, published by Addison Wesley. The most recent edition is the 6th, though you
may use an older edition as well. However, I will use the numbering of the 6th edition to refer
to sections and exercises in the book. If you use an older edition, it will be your responsibility
to “convert” to the corresponding sections and exercises in your edition.
Supplemental materials will be posted on the UNCG Canvas website. You must have an
active UNCG email account in order to access UNCG Canvas.
SOFTWARE : In this course we will analyze data and apply regression techniques using SAS
statistical software. There are several ways to access and use SAS:
1. You can download the SAS installation files via secure FTP from any location with
internet access. For this option you will need high-speed internet and no data download limits:
2. You can use SAS in any of the computer labs on campus.
3. You can access SAS remotely through the Virtual Computing Lab
(https://mycloud.uncg.edu/vpn/index.html). Log on using your UNCG username and password.
COURSE OBJECTIVES: By the end of the semester, you should be able to
 Understand regression analysis, including the interpretation of regression models, model
specification, ordinary least squares estimation, and the assumptions of the classical
 Conduct statistical hypothesis tests and construct confidence intervals;
 Understand violations of the classical model assumptions, such as heteroscedasticity, and
methods for dealing with them;
 Understand and interpret a number of alternative regression models, including models
with dummy and non-linear independent variables;
 Use SAS to analyze actual data, estimate regression models, make predictions and infer
causal relationships between economic variables.
STUDENT RESPONSIBILITIES : Over the course of the semester I expect you to:
 Attend class regularly. This is not the type of class where you can skip lectures, expecting
to “cram” the week before an exam. The material is difficult and cumulative, and not
everything I teach is covered in the textbook. Missing even one class could be
detrimental. I expect regular attendance. You are allowed to miss at most 5 classes during
the semester. After 5 missed classes, I will deduct 2 percentage points from your course
grade for each class you miss. Furthermore, you will receive a score of 0 for all
assignments that you miss due to being late or absent.
 Arrive on time. I am aware that traffic can be congested and that parking on campus can
be difficult. However, these are not valid excuses for being late. I reserve the right to
count you as absent if you are late.
 Come to class prepared. Reading the textbook will be extremely helpful. I may assign
readings in addition to the relevant textbook chapters. You are responsible for all of these
 Spend a minimum of 5 – 6 hours each week reading, reviewing and completing
homework assignments in addition to attending lectures and labs. If this is not feasible for
you given your other time commitments, then this is probably not the class for you.
 Refrain from using cell phones and talking to your neighbors during class. I reserve the
right to ask you to leave the room if I think that you are being disruptive and disrespectful.
 Approach me with any concerns you have about the class and ask questions during
lecture when needed. If you are struggling with any part of the material, it is extremely
likely that you are not alone. It is best if I know sooner rather than later.
GRADES: Your grade will be based on the following components:
Problem sets
Lab assignments
Midterm exam
Final exam
Problem sets: There will be 4 problem sets. Each will receive a grade of 0 (= no work or
insufficient work), 1 (= substantial work but many incorrect answers), or 2 (= complete work
with (mostly) correct answers). The problem sets will be posted on Canvas. You can work on
the homework assignments in groups but you have to write up your own solutions. Identical
assignments will all receive a score of zero. All homework is due at the beginning of class;
no late homework will be accepted. If you have a valid excuse to miss class you can make
arrangements with me to turn in your homework early.
I will post an answer key on Canvas after your work has been turned in. Please review this key
and come to the next class prepared to ask any questions you may have. Working through the
homework problems thoroughly and completely is the best way to learn this material.
Lab assignments: There will be 4 lab assignments throughout the semester. These assignments
will build on the work we do in the computer labs. As with the problem sets, lab assignments
(and answer keys) will be posted on Canvas and will receive a grade of 0, 1, or 2. Under no
circumstances will late lab assignments be accepted. Again, I encourage you to work in groups
on these assignments, but all submitted work must be your own.
Quizzes: There will be 4 quizzes throughout the semester. Topics covered by each quiz will
be announced one week in advance. The quiz dates are:
 Quiz 1: Tuesday, January 26
 Quiz 2: Thursday, February 11
 Quiz 3: Thursday, March 24
 Quiz 4: Tuesday, April 12
Exams: There will be one in-class midterm on Thursday, March 3 and a cumulative final
exam from 12pm to 3pm on Thursday, April 28. There will be no make-ups for the exams.
If you have a valid excuse (e.g. a doctor’s note), you have the option to take an oral exam
that I will administer. You must let me know prior to the exam if you will have to miss it.
Otherwise you will receive a score of 0.
No extra credit assignments will be available at the end of the semester. All grade disputes
must be addressed in writing.
I utilize the following scale to convert number into letter grades:
98% to 100%
93% to 97.9%
A90% to 92.9%
86% to 89.9%
83% to 85.9%
B80% to 82.9%
76% to 79.9%
73% to 75.9%
C70% to 72.9%
66% to 69.9%
63% to 65.9%
D60% to 62.9%
below 60%
I do not round the numbers. For example, a final score of 85.9% corresponds to a B, but a
score of 86.0% will guarantee a B+.
ACADEMIC INTEGRITY POLICY: Students are expected to know and abide by UNCG’s
Academic Integrity Policy in all matters pertaining to this course. Violations will be pursued
in accordance with the Policy. The link to UNCG’s academic integrity policy is:
http://www.uncg.edu/bae/faculty_student_guidelines.pdf. Please read them carefully.
(Subject to change)
The topics discussed in the course are listed below. The corresponding chapters in the textbook are
given in parentheses.
Week 1 – January 12
What is Econometrics? (Section 1.1)
Review of Statistics & Hypothesis Testing (Chapter 17)
Week 2 – January 19
Review of Statistics & Hypothesis Testing (Chapter 17)
Week 3 – January 26
1/26 – Quiz 1
 Introduction to Regression Analysis (Chapter 1)
2/2 – Problem Set 1 due
 Ordinary Least Squares (Chapter 2)
 Applying Regression Analysis (Chapter 3)
2/11 – Quiz 2
 Sampling Distributions of Estimators (Section 4.2)
2/18 – Lab Assignment 1 due
 The Classical Model (Chapter 4)
2/25 – Problem Set 2 due
3/3 – Midterm Exam
SPRING BREAK: March 5 – March 13
Week 4 – February 2
Week 5 – February 9
Week 6 – February 16
Week 7 – February 23
Week 8 – March 1
Week 9 – March 15
Week 10 – March 22
3/22 – Lab Assignment 2 due
 Specification: Choosing the Independent Variables (Chapter 6)
3/24 – Quiz 3
3/29 – Problem Set 3 due
 Specification: Choosing a Functional Form (Chapter 7)
Week 11 – March 29
Hypothesis Testing (Chapter 5)
Week 12 – April 5
 Violations of the Classical Assumptions: Heteroscedasticity (Chapter 10)
4/7 – Lab Assignment 3 due
Week 13 – April 12
 Violations of the Classical Assumptions: Multicollinearity (Chapter 8)
4/14 – Quiz 4
 Violations of the Classical Assumptions: Serial Correlation (Chapter 9)
4/21 – Problem Set 4 due
4/26 – Lab Assignment 4 due
Final Exam: 12 noon – 3pm on Thursday, April 28
Week 14 – April 19