ECONOMICS 643: ECONOMETRIC METHODS
Fall 2014
TR 11am – 12:15pm
456 Bryan Building
INSTRUCTOR: Professor Dora Gicheva
Office: 459 Bryan Building
Phone: 334-4865
E-mail: d_gichev@uncg.edu
Office Hours: By appointment
LAB: TR 12:30pm-1:45pm, Bryan 211
COURSE DESCRIPTION: This course, oriented towards applied practitioners, provides an
introduction to many of the tools commonly used in econometric analysis. The main focus is
on research design, implementation and microeconomic applications, rather than theoretical
proofs. The goal of the course is for students to become familiar with a set of useful
statistical techniques and learn how to use them to identify correlations and causal
relationships using the SAS software system.
COURSE MATERIALS: The required textbooks for this course are:
Introductory Econometrics: A Modern Approach (5th edition) by Jeffrey Wooldridge
(ISBN 978-1111531041)
The Little SAS Book (5th edition) by Lora D. Delwiche and Susan J. Slaughter (ISBN
978-1612904009)
There will be additional readings mainly from journal articles. I will make such readings
available on the course website.
I will usually post brief lecture notes on the course website prior to lecture. You should
print them out and go over them before class. Bringing them to class will make it easier for
you to take notes.
The required software for this class is SAS®.
COURSE OBJECTIVES: By the end of the semester, students should have the econometric
background necessary to conduct competent applied econometric analysis and interpret
statistical results using data sets in microeconomics or related fields. Students will learn
about the following topics and the implementation of the corresponding statistical
models in SAS:
 Simple and multiple linear regression model
 Nonlinear regression models, including the logarithmic and semi-logarithmic models,
polynomials and interaction terms
 Dummy independent variables, as well as the linear probability model
 Policy analysis using the difference-in-difference estimator
 Measurement error and omitted variable bias
 Heteroskedasticity and other issues with the regression standard errors
 Simultaneous equations and two-stage least squares estimation
GRADES: Grades will be based on the following components:
Midterm exam
Final exam
Homework
Empirical project
Class participation
25%
25%
20%
15%
15%
Midterm: The midterm will be given in class on Thursday, October 9. There will be no
exceptions.
Final Exam: The final will be the same length as the midterm and will include the material
covered after the midterm. It will be given during the University-specified time (Tuesday,
December 9, 12pm – 2pm)
Homework: Problem sets will usually be assigned bi-weekly. Once assigned, you will have
two weeks to complete and turn in the homework. Most problem sets with have a
programming component, which should be completed using SAS®. You should turn in a
printout of your (well-documented and clearly written) program, as well as a printout of the
output. You can work on the homework assignments in groups but you have to write up your
own solutions. No late homework will be accepted.
Empirical Project: More details about the empirical project will be provided separately. It
will be due before our Thanksgiving break.
Class participation: Your participation grade will depend on class attendance and
preparation. I expect you to read all assigned papers before class and to participate in in-class
discussions. I will ask questions about the reading. The participation grade includes a
presentation on Thursday, August 28. There is a handout on the course website with details
about the presentation.
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://sa.uncg.edu/handbook/academic-integrity-policy/
FACULTY AND STUDENT GUIDELINES can be found at http://bae.uncg.edu/wpcontent/uploads/2012/08/faculty_student_guidelines.pdf. Please read them carefully.