ECONOMICS 351 Economics and Business Statistics II Spring 2015

Economics and Business Statistics II
Spring 2015
MWF 12:00PM – 12:50PM
111 Bryan Building
Office hours:
Dr. Martijn van Hasselt
Bryan 446
Mondays, 1:30PM – 2:30PM
Wednesdays, 1:30PM – 3:00PM
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.
Course Text
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.
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 purchase a copy for installation on your own machine through UNCG’s online
software store ( The cost of an
academic license is currently $15.
2. You can use SAS in any of the computer labs on campus.
3. You can access SAS remotely through the Virtual Computing Lab
( Log on using your UNCG username and
Course Objectives
By the end of the semester, you should be able to
1. Understand regression analysis, including the interpretation of regression models, model
specification, ordinary least squares estimation, and the assumptions of the classical
2. Conduct statistical hypothesis tests and construct confidence intervals;
3. Understand violations of the classical model assumptions, including multicollinearity,
serial correlation and heteroscedasticity, and methods for dealing with them;
4. Understand and interpret a number of alternative regression models, including models
with dummy dependent variables or non-linear independent variables;
5. Use SAS to analyze actual data, estimate regression models, make predictions and infer
causal relationships between economic variables.
Class Conduct
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, so 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.
I expect you to come to class prepared. You should read the relevant material before I cover it in
lecture and come to class ready to work out problems and ask/answer questions. Make the best use
of your time! Come to lecture prepared to take an active part in your learning. I strongly encourage
and welcome questions. There is no such thing as a stupid question (the only thing that is stupid is
to not ask questions when you have them!).
In addition to attending lectures and labs, you are expected to spend a minimum of 5-6 hours each
week reading, reviewing and completing homework assignments. If this is not feasible for you
given your other time commitments, then this is probably not the class for you.
Lateness will not be tolerated. 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 and it is your responsibility to
arrive on time. I reserve the right to count you as absent if you are late.
Do not talk to your neighbors during class. It distracts the students around you, and it distracts me.
It will not be tolerated. Please also make sure that all cell phones are shut off during class. Texting
during class is not permitted. If I see you texting, I reserve the right to ask you to leave the room.
Your final grade will be determined based on your performance on the following.
 Problem sets
 Lab assignments
 Quizzes
 Midterm exam
 Final exam
Problem Sets
There will be approximately 4 problem sets throughout the semester. 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
( and are due at the beginning of class on the specified due date.
Under no circumstances will late homework be accepted. If you anticipate missing class on the
day homework is due, you must get the assignment to me before the start of class. This can be
accomplished by leaving the assignment in the bin outside my office door.
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. I
encourage you to work in groups on these assignments. However, all submitted work must be
your own. Do not just copy down answers from a friend! If I see/suspect/discover that this is
occurring, I reserve the right to assign all parties involved an F for that assignment.
Lab Assignments
There will be approximately 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 (see the above notice regarding
violations of this policy).
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: Monday, February 2nd
 Quiz 2: Friday, February 20th
 Quiz 3: Wednesday, April 1st
 Quiz 4: Friday, April 17th
There will be one in-class midterm on Wednesday, March 4th and a cumulative final exam on
Friday, May 1st from 12:00PM to 3:00PM.
Important Dates
Monday, January 12th: first day of classes
Monday, March 9th – Friday, March 13th: no classes (Spring Break)
Friday, April 3rd: no ECO351 class (Spring holiday)
Tuesday, April 28th: last day of classes (the university follows a Friday schedule)
Academic Integrity Policy
Students are expected to know and abide by the Academic Integrity Policy in all matters
pertaining to this course. Violations of this code will be pursued in accordance with the code.
The link to UNCG’s academic integrity policy is:
Faculty and Student Guidelines
Please familiarize yourself with the Bryan School’s Faculty and Student Guidelines. These
guidelines establish principles and expectations for the administration, faculty, staff, and students
of the Bryan School of Business and Economics. The link for this document is:
Course Outline
The topics discussed in the course are listed below. The corresponding chapters in the textbook
are given in parentheses.
What is Econometrics? (Section 1.1)
Review of Statistics & Hypothesis Testing (Chapter 17)
Introduction to Regression Analysis (Chapter 1)
Ordinary Least Squares (Chapter 2)
Applying Regression Analysis (Chapter 3)
Sampling Distributions of Estimators (Section 4.2)
The Classical Model (Chapter 4)
Hypothesis Testing (Chapter 5)
Specification: Choosing the Independent Variables (Chapter 6)
Specification: Choosing a Functional Form (Chapter 7)
Violations of the Classical Assumptions: Heteroscedasticity (Chapter 10)
Violations of the Classical Assumptions: Multicollinearity (Chapter 8)
Violations of the Classical Assumptions: Serial Correlation (Chapter 9)
Dummy Dependent Variables (Chapter 13)