Econometric Methods (Eco 643)
Fall 2015
Instructor: Prof. Marie Hull
Bryan 445
mchull2@uncg.edu
Office hours: Monday 2-3pm and by appointment
TA: Chris Parrish
Bryan 470
clparri3@uncg.edu
Office hours: Tuesday 2-3pm and by appointment
Lecture: TR 11:00am-12:15pm
Bryan 456
Lab: Some Thursdays instead of lecture, 12:30-1:45pm
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.
Prerequisites: Eco 351, Eco 619, or permission of instructor
Course materials
Introductory Econometrics: A Modern Approach (5th ed.) by Jeffrey Wooldridge
ISBN 978-1111531041
The Little SAS Book (5th ed.) by Lora D. Delwiche and Susan J. Slaughter
ISBN 978-1612904009
SAS software, available in UNCG computer labs. You may also purchase license for your
personal computer through ITS.
Supplemental readings, available on Canvas
Lecture notes, available on Canvas
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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 models
• 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-differences estimator
• Measurement error and omitted variable bias
• Heteroskedasticity and other issues with the regression standard errors
• Simultaneous equations and two-stage least squares estimation
Evaluation
Check-in quiz
Midterm exam
Empirical project
Final exam
Homework
Class participation
September 17, in class
October 8, in class
November 24
December 8, 12-3pm
Every two weeks
Ongoing
5%
30%
10%
30%
15%
10%
Check-in quiz: The check-in quiz is designed to give both of us feedback on your
understanding of the course material early on. It will be given during normal class time, and you
will have 45 minutes to complete the quiz. Note that we have a lab scheduled that day.
Midterm exam: The midterm exam includes material covered from the beginning of the
semester.
Final exam: The final exam will include material covered after the midterm.
Empirical project: The empirical project requires that you replicate a published article and write
up the results. More details on the project will be provided later.
Homework: You will have a homework assignment approximately every other week. Any
programming components should be completed in SAS. Turn in a copy of your program and a
copy of your output. You make work on the homework in groups, but you must write up your
own solutions. No late homework will be accepted.
Class participation: Your participation grade depends on several factors. The first is regular
class attendance. The second is engagement with the course material through interactions with
the professor and your classmates. This will usually take the form of in-class comments and
questions, but you may also email me questions that might benefit the rest of the class. A
prerequisite for in-class engagement is completing all the required reading and assignments
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before coming to class. The final parts of your participation grade come from a short
presentation and posting on Canvas. Before each class with a supplemental reading, post a
response to the reading (a few sentences will do) on the Discussions page of the Canvas site.
Academic Integrity
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. For more
information, see: http://sa.uncg.edu/handbook/academic-integrity-policy/
Faculty and Student Guidelines
Please review the faculty and student guidelines at: http://bae.uncg.edu/wp-content/uploads/
2012/08/faculty_student_guidelines.pdf
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Course Outline
Class dates
Topics
Readings
August 18 & 20
Introduction to Econometrics
• Structure of economic data
• Correlation vs. causation
Presenting Descriptive Statistics
• Wooldridge, Ch. 1
• Taubes, Gary. 2007. “Do We Really Know
What Makes Us Healthy?” NY Times.
August 25 & 27
Student Presentations
Simple Linear Regression Model
• Schwabisch, Jonathan A. 2014. “An
Economist’s Guide to Visualizing Data.”
JEP 28(1), 209-34.
• Trends in Student Aid 2014 (College Board
Report)
• Wooldridge, Ch. 2 & 6.1
September 1 & 3
Multiple Linear Regression
• Model and motivation
• Interpretation of coefficients
Lab: Intro to SAS, simple
regression
• Wooldridge, Ch. 3
• DiNardo, J. and J.S. Pischke. 1997. “The
Returns to Computer Use Revisited: Have
Pencils Changed the Wage Structure Too?”
QJE 112(1), 291-303.
September 8 & 10
Multiple Linear Regression
• Properties of coefficients
• Hypothesis testing
Wooldridge, Ch. 3 & 4
September 15 & 17 Multiple Linear Regression
• Dealing with multicollinearity
• Testing linear restrictions
• Goodness of fit
Check-in quiz
Lab: Multiple regression in SAS,
reporting regression results
• Wooldridge, Ch. 4 & 6.3
• Geiser, Saul and Maria Veronica Santelices.
2007. “Validity of High-School Grades in
Predicting Student Success beyond the
Freshman Year: High-School Record vs.
Standardized Tests and Indicators of FourYear College Outcomes.” CSHE.6.07.
September 22 & 24 Outliers and Influential
Wooldridge, Ch. 2.4, 6, 9.1, 9.5
Observations
Nonlinear Regression Models
• Logarithmic & semi-logarithmic
• Polynomials & interaction terms
• Model selection
September 29 &
October 1
Dummy Independent Variables
• Importance
• Interpretation
• Interactions
Lab: Program evaluation with
nonrandom assignment
• Wooldridge, Ch. 7.1-7.4
• Durden, Gary C. and Larry V. Ellis. 1995.
“The Effects of Attendance on Student
Learning in Principles of Economics.” AER
85(2), 343-46.
• For Lab: Wooldridge, Ch. 7.6
October 6 & 8
Linear Probability Model
Midterm Exam
Wooldridge, Ch. 7.5 & 7.7
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Class dates
Topics
Readings
October 15
Differences-in-Differences
Estimator
• Policy analysis with pooled
cross-sections
Wooldridge, Ch. 13.1 & 13.2
October 20 & 22
Difference-in-Differences
• Policy analysis with two-period
panel data
Lab: Diff-in-diff estimation
• Wooldridge, Ch. 13.3-13.5, Appendix 13A,
Ch. 14.4
• Card, David and Alan B. Krueger. 1994.
“Minimum Wages and Employment: A Case
Study of the Fast-Food Industry in New
Jersey and Pennsylvania.” AER 84(4),
772-93.
October 27 & 29
Proxy Variables
Measurement Error and Missing
Data
• Imputing missing data
Wooldridge, Ch. 9.2, 9.4-9.5
November 3 & 5
Heteroskedasticity
• Estimating robust standard
errors
• Weighted least squares
• Bootstrapping
Lab: Calculating corrected
standard errors
Wooldridge, Ch. 8, Appendix 6A (pp. 225-26)
November 10 & 12
Simultaneous Equations
• Example: estimating a demand
curve
• Using a Monte Carlo
experiment to find the bias
Instrumental Variables and TwoStage Least Squares Estimation
Wooldridge, Ch. 15, 16.1-16.4
November 17 & 19
Instrumental Variables and TwoStage Least Squares Estimation
Lab: Monte Carlo experiments
and IV estimation
McClellan, M., B.J. McNeil, and J.P.
Newhouse. 1994. “Does More Intensive
Treatment of Acute Myocardial Infarction in
the Elderly Reduce Mortality?” JAMA 272(11):
859-66.
November 24
Makeup/Review Day
Empirical Project due
December 8
Final Exam (12-3pm)
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