ECON 6394: Topics in Applied
Econometrics
Spring 2006 Tuesdays
and Thursdays
Course
Syllabus
Professor Aimee Chin
University of Houston Department of Economics
office hours: Fridays
course homepage: http://www.uh.edu/~achin/courses for announcements and assignments
Course Description
The purpose of this course is to expose students to some econometric techniques frequently used in applied microeconomic research. The course features critical reading of empirical research papers and the implementation of econometric methods on actual data sets.
Requirements and
Grading
|
1) 6 Problem Sets |
Approximately every 1-2 weeks |
60% |
|
2) In-class Exam |
Tuesday April 18 |
20% |
|
3) Student
Presentation |
Last weeks of the semester |
10% |
|
4) Class Participation |
Combination of attendance and quality of classroom comments |
10% |
Problem Sets: For the data exercises, we will use Stata. Students are encouraged to work together on problem sets. However, each student must write up his/her own problem set. No copies will be accepted, and this includes programs.
Student Presentation: Each student will lead a discussion of an empirical paper in class. Papers will be assigned and written instructions for the presentation will be provided before Spring break.
In-Class Exam: There will be a closed-book exam during our class meeting on April 18 covering all the materials of Part I and Part II of the course.
Prerequisites
Students must have completed ECON 7331 (Econometrics I).
There is no required textbook
for this course. Two recommended textbooks
are:
Wooldridge,
Jeffrey, Econometric Analysis of Cross
Section and Panel Data,
Shadish,
William R., Thomas D. Cook and Donald T. Campbell, Experimental and Quasi-Experimental Designs for Generalized Causal
Inference,
Course Schedule (subject to change)
Part I. Some Basics
Lecture 1. Introduction
and review of OLS (1/17)
Wooldridge, Chapters 1-4
or Greene Econometric
Analysis 5th edition, Chapters 1-6 & Appendices C and D
Lecture 2.
Why the OLS estimator may be inconsistent (1/19)
Wooldridge, Chapters 1-4
or Greene, Chapters 1-6 & Appendices C and D
Lecture 3. Some
panel data models (1/24, 1/26)
Wooldridge, Chapter 10
or Greene, Chapter 13
Lecture 4.
Some discrete response models (1/31, 2/2)
Wooldridge, Chapter 15
or Greene, Chapter 21
Part
II. Program Evaluation and Causal Inference
Wooldridge, Chapter 18
Shadish, Cook and Campbell
Lecture 5.
The Evaluation Problem (2/7)
Angrist, Joshua D. and Alan B. Krueger (1999), “Empirical
Strategies in Labor Economics,” Handbook of Labor Economics, Volume 3, Ashenfelter, A. and D. Card, eds.,
Lecture 6.
Randomized Experiments (2/9, 2/14, 2/16)
Burtless, G. (1995), “The Case for Randomized Field Trials in
Economic and Policy Research,” Journal of Economic Perspectives,
9(2):63-84. (JSTOR)
Woodbury, Stephen A. and Robert G. Spiegelman (1987), “Bonuses to Workers and Employers to
Reduce Unemployment: Randomized Trials in
Meyer, Bruce D. (1995), “Lessons from the
Krueger, Alan B. (1999), “Experimental
Estimates of Educational Production Functions,” Quarterly Journal of
Economics, 114(2):497. (JSTOR)
Angrist, Joshua, Eric Bettinger,
Eric Bloom, Elizabeth King and Michael Kremer (2002),” American Economic Review, 92(5):1535-1558. Go through UH
Libraries Individual Journals, find AER, select a full
text option like EBSCO)
Sacerdote, Bruce (2001), “Peer Effects with Random Assignment:
Results for
Miguel, Edward and Michael Kremer (2004), “
URL: http://emlab.berkeley.edu/users/emiguel/miguel_worms.pdf
Lecture 7. Controlling
for Confounding Variables (Regression, Matching) (2/21, 2/23, 2/28)
Rosenbaum, Paul R., and Donald B. Rubin
(1983), “The Central Role of the Propensity Score in Observational Studies for Causal
Effects,” Biometrika, 70(1): 41-55.
Angrist, Joshua D. (1998), “Estimating the Labor Market Impact
of Voluntary Military Service Using Social Security Data on Military
Applicants,” Econometrica 66(2):249-288.
Heckman, James J., Hidehiko Ichimura and
Ashenfelter, Orley and David Card
(1985), “Using the Longitudinal Structure of Earnings to Estimate the Effect of
Training Programs,” Review of Economics and Statistics 67(4):648-660.
Lalonde, Robert J. (1986), “Evaluating the Econometric
Evaluations of Training Programs Using Experimental Data,” American Economic
Review 76(4):604-620.
Dehejia, Rajeev H. and Sadek Wahba (1999), “Causal Effects in Nonexperimental
Studies: Reevaluating the Evaluation of Training Programs,” Journal of the
American Statistical Association, 94:1053-1062.
Krueger, Alan and Orley
Ashenfelter (1994), “Estimates of the Economic Return
to Schooling from a New Sample of Twins,” American Economic Review
84(5):1157-1173.
Currie, Janet and Duncan Thomas, “Does Head
Start Make a Difference?,” American Economic Review
85(3):341-364.
Lecture 8. Difference-in-Differences
Strategies (3/2, 3/7, 3/9)
Meyer, Bruce D. (1995), “Natural
and quasi-experiments in economics,” Journal of Business and Economic
Statistics, 13(2):151-161.
Rosenzweig, Mark R. and Kenneth I. Wolpin
(2000), “Natural ‘Natural Experiments’ in Economics, Journal of Economic Literature, 38:827-874.
Meyer, Bruce D., W. Kip Viscusi
and David L. Durbin (1995), “Worker’s Compensation and Injury Duration:
Evidence from a Natural Experiment,” American Economic Review, 85(3):322-340.
(JSTOR)
Dynarski, Susan M. (2003), “Does Aid Matter? Measuring the Effect of Student Aid on
College Attendance and Completion,” American Economic Review, 93(1):279-288. (JSTOR)
Duflo, Esther (2001), “Schooling and labor market
consequences of school construction in
Friedberg, Leora
(1998), "Did Unilateral Divorce Raise Divorce Rates? Evidence from Panel Data," American
Economic Review, 88(3):608-627.
(JSTOR)
Lecture 9.
Instrumental Variables Estimation (3/21, 3/23, 3/28)
Wooldridge, Chapter 5
Angrist, Joshua D., Guido W. Imbens
and Donald B. Rubin (1996), “Identification of Causal Effects Using
Instrumental Variables,” Journal of the American Statistical Association,
91(434):444-455. (JSTOR)
Guido W. Imbens and
Joshua D. Angrist (1994), “Identification and
Estimation of Local Average Treatment Effects,” Econometrica,
62(2):467-475. (JSTOR)
Heckman, James, “Instrumental Variables: A
Study of Implicit Behavioral Assumptions Used in Making Program Evaluations,” Journal
of Human Resources, 32(3):441-462.
(A reply from Angrist and Imbens
immediately follows the article) (JSTOR)
Angrist, Joshua D. and Alan B. Krueger (2001), “Instrumental
Variables and the Search for Identification: From Supply and Demand to Natural
Experiments,” Journal of Economic Perspectives, 13(2):69-85. (Use http://www.nber.org/papers/w8456)
Angrist, Joshua D. (1990), “Lifetime Earnings and the
McClellan, Mark, Barbara J.
McNeil and Joseph P. Newhouse (1994), “Does More
Intensive Treatment of Acute Myocardial Infarction in the Elderly Reduce
Mortality?,” Journal
of the American Medical Association 272(11):859-866.
Bleakley, C. Hoyt and Aimee Chin (2004), “Language Skills and
Earnings: Evidence from Childhood Immigrants,” Review of Economics and Statistics 86(2):481-496.
Lecture 10.
Regression Discontinuity Design (3/30, 4/4)
Campbell, Donald T. (1969),
“Reforms as experiments,” American
Psychologist, 24: 407-429.
Hahn, Jinyong,
Petra Todd and Wilbert van der Klaauw
(2001), “Identification and Estimation of Treatment Effects with a
Regression-Discontinuity Design,” Econometrica,
69(1):201-209. (JSTOR)
Angrist, Joshua D. and Victor Lavy
(1999), “Using Maimonides’ Rule to Estimate the
Effect of Class Size on Scholastic Achievement,” Quarterly Journal of
Economics, 114:533-575. (JSTOR)
Part
III. Additional Topics
Some possible topics include sample selection, kernel
estimation and quantile regression.
Part
IV. Student presentations