ECO 721-01: Empirical Microeconomics
Jeremy Bray
Bryan 462D
email: jwbray@uncg.edu
Course meeting time: MW 2:00-3:15
Location: Bryan 202
Office Hours: by appointment
Description
In this course you will apply the skills you have learned in your theory and econometrics classes
and learn new skills. The primary objective of the course is for you to learn how economists
conduct applied, empirical research. Empirical research involves more than mindlessly
estimating econometric models and tabling the results. You must learn how to develop and frame
meaningful research questions, place them in the appropriate economic context, and then apply
the best empirical methods to the most appropriate data to answer them. A secondary objective is
for you to learn how to learn new methods. Econometric methods are constantly evolving and
each empirical problem presents its own challenges, thus the most successful economists know
when they must learn a new technique and know how to teach it to themselves. To achieve these
objectives, you will learn new econometric techniques, read articles that apply these techniques,
and use these techniques yourself in labs, homework assignments, and an applied research paper.
Procedures
We will meet twice per week, usually for an hour and 15 minutes. Attendance is mandatory and
students are expected to come to class prepared to discuss interactively the assigned reading and
to have completed all assigned work prior to class. Your grade will be based on the following
criteria:
 Biweekly homework assignments (20%)
 Biweekly labs (20%)
 Exam 1 (20%)
 Exam 2 (20%)
 Research project (20%)
Homework assignments will include a mix of data assignments and critical assessments of
assigned readings. Labs will apply the methods and techniques covered in the preceding lectures.
The exams are cumulative. Information on the research project will be provided at a later date.
Software
The primary software package for this class will be SAS. SAS is installed in the UNCG
computer labs. SAS licenses for personal computers are available for UNCG students through
ITS. To begin the license process, connect to https://web.uncg.edu/researchaccess/secure/sas/sas.asp. We may also occasionally use Stata and Excel.
Academic Integrity
Students are expected to be familiar with and abide by the University’s Academic Integrity
Policy (see http://academicintegrity.uncg.edu/). Collaboration on homework and lab exercises is
allowed and encouraged, but students must turn in their own work. Collaboration on exams is not
allowed and will be treated as a violation of the Academic Integrity Policy. Collaboration on the
research project is allowed, but students must complete their own projects. Plagiarism, including
plagiarizing a classmate or allowing a classmate to plagiarize you, will not be tolerated.
Text books and readings
The only required text is Kennedy, P. (2008). A guide to econometrics. MIT press. Any edition
should suffice, but reading assignments will reference the sixth edition. Additional readings will
be assigned weekly and will generally be available via the Internet or Jackson Library. In
addition to these readings, you will need good reference books in a variety of econometric
techniques. Suggested references include:
Allison, P. (1999). Logistic regression using SAS®: theory and application. SAS Publishing.
Delwiche, L. D., & Slaughter, S. J. (2012). The Little SAS Book: A Primer: a Programming
Approach. SAS Institute.
Goldberger, A. S. (1991). A course in econometrics. Harvard University Press.
Long, J. S. (2009). The workflow of data analysis using Stata. Stata Press books.
Stokes, M. E., Davis, C. S., & Koch, G. G. (2000). Categorical data analysis using the SAS
system. SAS institute.
Wooldridge, J. (2012). Introductory econometrics: A modern approach. Cengage Learning.
Course Schedule
Class
Topics
January 12
Preliminaries
Applied
econometrics
Identification
Experimental
design
January 14
January 19
January 21
Review of common
estimators
Estimators and
estimates
MM
OLS
MLE
Readings/Homework
Kennedy Chapter 22
UNCG instructions for human subjects trainings,
http://integrity.uncg.edu/wpcontent/uploads/2012/08/InstructionsforRCRCITITr
aining107.docx; complete the CITI Student
Researcher Module, print and turn in your
completion certificate by 1/14/15
Kennedy, P. E. (2002). Sinning in the basement: What
are the rules? The ten commandments of applied
econometrics. Journal of Economic Surveys, 16(4),
569-589.
Kennedy Chapters 1 and 2
Holiday – no class
Lab
Data
Class
January 26
Topics
Dichotomous
outcomes
Linear
probability model
Logit
Probit
January 28
Categorical outcomes
Multinomial logit
February 2
Categorical outcomes
Ordered
logit/probit
February 4
February 9
Lab
Count data models
Poisson
Negative
binomial
February 11
Count data models
Poisson
Negative
binomial
Count data models
Zero-inflated and
hurdle models
Lab
February 16
February 18
Readings/Homework
Logit HW due 1/28
SAS proc qlim documentation
http://support.sas.com/documentation/cdl/en/etsug/6
7525/HTML/default/viewer.htm#etsug_qlim_toc.ht
m
Kennedy Chapter 16.1 through 16.3 (including
technical notes)
Ai, C., & Norton, E. C. (2003). Interaction terms in
logit and probit models. Economics letters, 80(1),
123-129.
Bray, J. W., Zarkin, G. A., Ringwalt, C., & Qi, J.
(2000). The relationship between marijuana
initiation and dropping out of high school. Health
Economics, 9(1), 9–18.
Mullahy, J., & Sindelar, J. (1989). Life-cycle effects of
alcoholism on education, earnings, and occupation.
Inquiry: a journal of medical care organization,
provision and financing, 26(2), 272.
Dunn, L. F., & Kim, T. (1999). An empirical
investigation of credit card default. Ohio State
University, Department of Economics Working
Papers, (99-13).
Logit/probit models
Count data HW due 2/11
SAS proc countreg documentation
http://support.sas.com/documentation/cdl/en/etsug/6
0372/HTML/default/viewer.htm#countreg_toc.htm
Kennedy Chapter 16.4 (including technical notes)
Cameron, A. C., Trivedi, P. K., Milne, F., & Piggott, J.
(1988). A microeconometric model of the demand
for health care and health insurance in Australia. The
Review of Economic Studies, 55(1), 85-106.
Mullahy, J. (1997). Heterogeneity, excess zeros, and
the structure of count data models. Journal of
Applied Econometrics, 12(3), 337-350.
Count data models
Class
February 23
Topics
Models for data with
excess zeros
Two part models
February 25
Models for data with
excess zeros
Sample selection
models
Models for data with
excess zeros
Tobit/censored
regression
March 2
March 4
March 9 & 11
March 16
March 18
Review of
experimental designs
Endogeneity
IV
Endogeneity
Control functions
Propensity scores
Readings/Homework
2PM HW due 2/25
Kennedy Chapter 17.1 though 17.3 (including technical
notes)
Manning, W. G., Newhouse, J. P., Duan, N., Keeler, E.
B., & Leibowitz, A. (1987). Health insurance and the
demand for medical care: evidence from a
randomized experiment. The American economic
review, 251-277.
Pacula, R. L. (1998). Does increasing the beer tax
reduce marijuana consumption?. Journal of health
economics, 17(5), 557-585.
Gill, A. M., & Michaels, R. J. (1991). Does drug use
lower wages. Indus. & Lab. Rel. Rev., 45, 419.
Greene, W. H., & Quester, A. O. (1982). Divorce risk
and wives labor supply behavior. Social Science
Quarterly, 63(1), 16-27.
Exam 1
Spring Break – no class
IV HW due 3/18
Kennedy Chapter 9
Freedman, D. (1991). Statistical Models and Shoe
Leather. Sociological Methodology 21, 291-313.
Heckman, J. J. (2005). The scientific model of
causality. Sociological methodology, 35(1), 1-97.
Grossman, M., & Markowitz, S. (2002). I did what last
night?!!! Adolescent risky sexual behaviors and
substance use (No. w9244). National Bureau of
Economic Research.
Wooldridge, J. (2007). What’s New in Econometrics?
Lecture 6: Control Functions and Related Methods.
NBER Summer Institute.
Dehejia, R. H., & Wahba, S. (1999). Causal effects in
nonexperimental studies: Reevaluating the
evaluation of training programs. Journal of the
American statistical Association, 94(448), 10531062.
Class
March 23
Topics
Endogeneity
Differences in
difference
March 25
March 30
Lab
Panel data
Random effects
April 1
Panel data
Fixed effects
April 6
Panel data
RE/FE logit
April 8
April 13
Lab
Panel data
RE/FE count data
models
April 15
Clustered data
Population
averaged vs
subject
specific
effects
Sandwich
variance
estimator
Readings/Homework
Wooldridge, J. (2007). What’s New in Econometrics?
Lecture 10 Difference-in-Differences
Estimation. NBER Summer Institute.
Kaestner, R. (2000). A note on the effect of minimum
drinking age laws on youth alcohol
consumption. Contemporary Economic
Policy, 18(3), 315-325.
Causal effects
Panel data HW due 4/1
Kennedy Chapter 18
SAS proc panel documentation
http://support.sas.com/documentation/cdl/en/etsug/6
0372/HTML/default/viewer.htm#etsug_panel_sect00
1.htm
Currie, J., & Fallick, B. (1993). The minimum wage and
the employment of youth: evidence from the
NLSY (No. w4348). National Bureau of Economic
Research.
Bray, J. W., Loomis, B. R., & Engelen, M. A. (2009).
You save money when you buy in bulk: Does
volume-based pricing cause people to buy more
beer? Health Economics, 18, 607–618.
Zarkin, G. A., Bray, J. W., & Qi, J. (2000, April). The
effect of employee assistance programs use on
healthcare utilization. Health Services Research,
35(1 Part I), 77–100.
Panel data
Clustered data HW due 4/15
Hausman, J., & Bronwyn, H. Hall, and Zvi Griliches.
1984.‘‘Econometric Models for Count Data with An
Application to the Patents-R&D
Relationship,’’52.Econometrica, 909-38.
Neuhaus, J. M., Kalbfleisch, J. D., & Hauck, W. W.
(1991). A comparison of cluster-specific and
population-averaged approaches for analyzing
correlated binary data. International Statistical
Review/Revue Internationale de Statistique, 25-35.
Class
April 20
Topics
Clustered data
Population
averaged vs
subject
specific
effects
Sandwich
variance
estimator
Lab
April 22
April 27
Research paper due on final exam date
Readings/Homework
Bray, J. W., Zarkin, G. A., Davis, K. L., Mitra, D.,
Higgins-Biddle, J. C., & Babor, T. F. (2007). The
health care utilization effect of screening and brief
intervention for risky drinking in four managed care
organizations. Medical Care, 45(2), 177–182.
Population averaged versus subject specific
Exam 2