Lehigh University
Department of Economics
Muzhe Yang
Fall 2015
ECONOMICS 464: APPLIED ECONOMETRICS (I)
Instructor: Muzhe Yang
Course Time: Tuesday and Thursday, 1:10–2:25 PM
Course Location: RBC 61
Course Website: Course Site (https://coursesite.lehigh.edu/)
Contact Information: [email protected], (610) 758-4962
O¢ ce Hours: Wednesday 10:30–11:30 AM, Tuesday and Thursday 5:00–6:00 PM, at RBC 456
Course Readings:
(1) Required: (a) Angrist and Pischke: Mostly Harmless Econometrics: An Empiricist’s Companion,
2008 (MHE, http://www.mostlyharmlesseconometrics.com); (b) A. Colin Cameron and Pravin
K. Trivedi: Microeconometrics: Methods and Applications, 2005 (MMA, http://cameron.econ.
ucdavis.edu/mmabook/mma.html); and (c) A. Colin Cameron and Pravin K. Trivedi: Microeconometrics Using Stata, Revised Edition, 2010 (MUS, http://cameron.econ.ucdavis.edu/
musbook/mus.html) .
(2) Recommended: (a) Myoung-jae Lee: Micro-Econometrics for Policy, Program, and Treatment Effects, 2005; (b) Je¤rey Wooldridge: Econometric Analysis of Cross Section and Panel Data, 2nd
Edition, 2010 (http://mitpress.mit.edu/books/econometric-analysis-cross-section-and-panel-data);
and (c) Paul Rosenbaum: Design of Observational Studies, 2010 (http://www-stat.wharton.
upenn.edu/~rosenbap/dosBookReviewBiometrics2010.pdf).
(3) References in addition to (1) and (2).
Course Prerequisite: ECO 416 or equivalent.
Course Requirements: Students are expected to read assigned readings and attend all lectures
because some class materials will not be in the readings. There will be 10 problem sets, most of
which require using Stata. Feel free to work cooperatively. However, each student must turn in
his or her own problem set using his or her own words and interpretation of the results. Late
problem sets will not be accepted.
Course Grading: (1) problem sets: 75%; (2) class participation: 5%; and (3) …nal exam (takehome): 20%. Problem sets will be graded using a 0–5 ordinal scale: 5 = excellent; 1 = poor;
and 0 = not handed in.
Course Overview and Objectives
In most of economics we are interested in causal, rather than correlative, relations among variables.
For example, it is not the correlation between earnings and years of schooling that is of interest, but
the e¤ect of increasing someone’s schooling by one year on that same person’s earnings. Microeconometrics focuses on identifying such a causal relationship using cross-sectional or short panel data.
It is very often that the heterogeneity of economic relations across individuals, …rms and industries
confounds correlations with causal relations. This course aims to: (1) familiarize students with conditions which are required for credible inference for causal e¤ects; and (2) enable students to select
1
08/12/2015 version
Lehigh University
Department of Economics
Muzhe Yang
Fall 2015
appropriate econometric tools for empirical economics problems and policy research. Topics include
robust inference, numerical optimization, instrumental variables, nonparametric regression methods,
treatment e¤ect analysis, and panel data.
By the end of the course, students should (1) be able to model the source of heterogeneities
according to the characteristics of micro-level data; (2) be familiar with approaches to causal inference
using the potential outcomes framework; and (3) have a …rm grasp of types of research designs that
enable researchers to evaluate the credibility of the empirical evidence used for testing theories and
for evaluating policies.
Course Outline and Readings
Part I: Basics of Microeconometrics and Causal Inference
Overview of microeconometrics
– MHE 1; Handout
– Angrist, J. D. and A. B. Krueger (1999). “Empirical Strategies in Labor Economics.”
Handbook of Labor Economics (Chapter 23), Elsevier. Volume 3, Part 1: 1277–1366.
– Cobb-Clark, D.-A. and T. Crossley (2003). “Econometrics for Evaluations: An Introduction
to Recent Developments.” Economic Record 79 (247): 491–511.
– Imbens, G. W. and J. M. Wooldridge (2009). “Recent Developments in the Econometrics
of Program Evaluation.” Journal of Economic Literature 47 (1): 5–86.
– Rosenzweig, M. R. and K. I. Wolpin (2000). “Natural ‘Natural Experiments’in Economics.”
Journal of Economic Literature 38 (4): 827–874.
– Winship, C. and S. L. Morgan (1999). “The Estimation of Causal E¤ects from Observational Data.” Annual Review of Sociology 25: 659–706.
Overview of health econometrics
– Handout
Basics of treatment e¤ect analysis
– MHE 2; MMA 25.1–25.2; Handout
– Freedman, D. A. (1991). “Statistical Models and Shoe Leather.” Sociological Methodology
21: 291–313.
– Holland, P. W. (1986). “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–960.
– Meyherhoefer, C. and M. Yang (2011). “The Relationship between Food Assistance and
Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects.” Applied Economic Perspectives and Policy 33 (3): 304–344.
– Rubin, D. B. (1974). “Estimating Causal E¤ects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66 (5): 688–701.
Regression analysis
– MHE 3; MMA 4.1–4.5, 4.7, 11, 24.5; Handout
2
08/12/2015 version
Lehigh University
Department of Economics
Muzhe Yang
Fall 2015
– Ashenfelter, O. and A. Krueger (1994). “Estimates of the Economic Return to Schooling
from a New Sample of Twins.” American Economic Review 84 (5): 1157–1173.
– Cameron, A. C., J. B. Gelbach and D. L. Miller (2008). “Bootstrap-Based Improvements
for Inference with Clustered Errors.” Review of Economics and Statistics 90 (3): 414–427.
– Cameron, A. C., J. B. Gelbach and D. L. Miller (2011). “Robust Inference with Multiway
Clustering.” Journal of Business & Economic Statistics 29 (2): 238–249.
– Moulton, B. R. (1990). “An Illustration of a Pitfall in Estimating the E¤ects of Aggregate
Variables on Micro Units.” Review of Economics and Statistics 72 (2): 334–338.
– Wooldridge, J. M. (2003). “Cluster-Sample Methods in Applied Econometrics.” American
Economic Review 93 (2): 133–138.
Nonparametric regression
– MMA 9; MUS 2.6; Handout
– Blundell, R. and A. Duncan (1998). “Kernel Regression in Empirical Microeconomics.”
Journal of Human Resources 33 (1): 62–87.
Simulation
– MUS 4
Numerical optimization
– MMA 10; MUS 11; Handout
Part II: Selection on Observables
Controlling for covariates
– MMA 25.3; Handout
– Imbens, G. W. (2003). “Sensitivity to Exogeneity Assumptions in Program Evaluation.”
American Economic Review 93 (2): 126–132.
Inverse probability weighting
– Handout
– Wooldridge, J. M. (2007). “Inverse Probability Weighted Estimation for General Missing
Data Problems.” Journal of Econometrics 141 (2): 1281–1301.
Propensity score matching
– MMA 25.4; Handout
– Almond, D., K. Y. Chay and D. Lee (2005). “The Costs of Low Birth Weight.” Quarterly
Journal of Economics 120 (3): 1031–1083.
– LaLonde, R. J. (1986). “Evaluating the Econometric Evaluations of Training Programs
with Experimental Data.” American Economic Review 76 (4): 604–620.
3
08/12/2015 version
Lehigh University
Department of Economics
Muzhe Yang
Fall 2015
– Rosenbaum, P. R. and D. B. Rubin (1983). “The Central Role of the Propensity Score in
Observational Studies for Causal E¤ects.” Biometrika 70 (1): 41–55.
– Rosenbaum, P. R. and D. B. Rubin (1984). “Reducing Bias in Observational Studies Using
Subclassi…cation on the Propensity Score.”Journal of the American Statistical Association
79 (387): 516–524.
– Smith, J. A. and P. E. Todd (2005). “Does Matching Overcome LaLonde’s Critique of
Nonexperimental Estimators?” Journal of Econometrics 125 (1–2): 305–353.
Part III: Selection on Unobservables
Instrumental variables
– MHE 4; MMA 4.8-4.9, 25.7; MUS 6; Handout
– Angrist, J. D. and A. B. Krueger (1991). “Does Compulsory School Attendance A¤ect
Schooling and Earnings?” Quarterly Journal of Economics 106 (4): 979–1014.
– Angrist, J. D. and A. B. Krueger (2001). “Instrumental Variables and the Search for
Identi…cation: From Supply and Demand to Natural Experiments.” Journal of Economic
Perspectives 15 (4): 69–85.
– Angrist, J. D., G. W. Imbens and D. B. Rubin (1996). “Identi…cation of Causal E¤ects
Using Instrumental Variables.” Journal of the American Statistical Association 91 (434):
444–455.
– Bera, A. K. and Y. Bilias (2002). “The MM, ME, ML, EL, EF and GMM Approaches to
Estimation: A Synthesis.” Journal of Econometrics 107 (1–2): 51–86.
– Bound, J., D. A. Jaeger and R. M. Baker (1995). “Problems with Instrumental Variables
Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak.”Journal of the American Statistical Association 90 (430): 443–450.
– Imbens, G. W. and J. D. Angrist (1994). “Identi…cation and Estimation of Local Average
Treatment E¤ects.” Econometrica 62 (2): 467–475.
Regression discontinuity design
– MHE 6; MMA 25.6; Handout
– Angrist, J. D. and V. Lavy (1999). “Using Maimonides’ Rule to Estimate the E¤ect of
Class Size on Scholastic Achievement.” Quarterly Journal of Economics 114 (2): 533–575.
– Chay, K. Y., P. J. McEwan and M. Urquiola (2005). “The Central Role of Noise in Evaluating Interventions that Use Test Scores to Rank Schools.”American Economic Review 95
(4): 1237–1258.
– Cook, T. D. (2008). “‘Waiting for Life to Arrive’: A History of the Regression-Discontinuity
Design in Psychology, Statistics and Economics.” Journal of Econometrics 142 (2): 636–
654.
– Hahn, J., P. Todd and W. V. der Klaauw (2001). “Identi…cation and Estimation of Treatment E¤ects with a Regression-Discontinuity Design.” Econometrica 69 (1): 201–209.
– Imbens, G. W. and K. Kalyanaraman (2012). “Optimal Bandwidth Choice for the Regression Discontinuity Estimator.” Review of Economic Studies 79 (3): 933–959.
4
08/12/2015 version
Lehigh University
Department of Economics
Muzhe Yang
Fall 2015
– Imbens, G. W. and T. Lemieux (2008). “Regression Discontinuity Designs: A Guide to
Practice.” Journal of Econometrics 142 (2): 615–635.
– Lee, D. S. (2008). “Randomized Experiments from Non-Random Selection in U.S. House
Elections.” Journal of Econometrics 142 (2): 675–697.
– Lee, D. S. and T. Lemieux (2010). “Regression Discontinuity Designs in Economics.”
Journal of Economic Literature 48 (2): 281–355.
– Thistlethwaite, D. L. and D. T. Campbell (1960). “Regression-Discontinuity Analysis: An
Alternative to the Ex Post Facto Experiment.” Journal of Educational Psychology 51 (6):
309–317.
Di¤erence-in-di¤erences
– MHE 5; MMA 25.5
– Athey, S. and G. W. Imbens (2006). “Identi…cation and Inference in Nonlinear Di¤erencein-Di¤erences Models.” Econometrica 74 (2): 431–497.
– Bertrand, M., E. Du‡o and S. Mullainathan (2004). “How Much Should We Trust Di¤erencesin-Di¤erences Estimates?” Quarterly Journal of Economics 119 (1): 249–275.
– Card, D. and A. B. Krueger (1994). “Minimum Wages and Employment: A Case Study of
the Fast-Food Industry in New Jersey and Pennsylvania.” American Economic Review 84
(4): 772–793.
– Gruber, J. (1994). “The Incidence of Mandated Maternity Bene…ts.” American Economic
Review 84 (3): 622–641.
– Meyer, B. D. (1995). “Natural and Quasi-Experiments in Economics.”Journal of Business
and Economic Statistics 13 (2): 151–161.
Linear panel models
– MHE 5; MMA 21
Linear panel models: extensions
– MMA 22; MUS 9
Quantile regression
– MHE 7; MMA 4.6; MUS 7.1-7.3
Control function approaches
– Angrist, J. D. (2001). “Estimation of Limited Dependent Variable Models With Dummy
Endogenous Regressors.” Journal of Business and Economic Statistics 19 (1): 2–28.
– Chay, K. Y. and M. Greenstone (2005). “Does Air Quality Matter? Evidence from the
Housing Market.” Journal of Political Economy 113 (2): 376–424.
– Garen, J. (1984). “The Returns to Schooling: A Selectivity Bias Approach with a Continuous Choice Variable.” Econometrica 52 (5): 1199–1218.
5
08/12/2015 version
Lehigh University
Department of Economics
Muzhe Yang
Fall 2015
– Heckman, J. J. (1979). “Sample Selection Bias as a Speci…cation Error.” Econometrica 47
(1): 153–161.
Estimation of treatment e¤ects
– Heckman, J. J. (2010). “Building Bridges between Structural and Program Evaluation
Approaches to Evaluating Policy.” Journal of Economic Literature 48 (2): 356–398.
– Imbens, G. W. (2010). “Better LATE Than Nothing: Some Comments on Deaton (2009)
and Heckman and Urzua (2009).” Journal of Economic Literature 48 (2): 399–423.
– Deaton, A. (2010). “Instruments, Randomization, and Learning about Development.”Journal of Economic Literature 48 (2): 424–455.
Con out of economics
– Angrist, J. D. and J.-S. Pischke (2010). “The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics.” Journal of
Economic Perspectives 24 (2): 3–30.
– Leamer, E. E. (2010). “Tantalus on the Road to Asymptopia.” Journal of Economic Perspectives 24 (2): 31–46.
– Keane, M. P. (2010). “A Structural Perspective on the Experimentalist School.” Journal
of Economic Perspectives 24 (2): 47–58.
– Sims, C. A. (2010). “But Economics Is Not an Experimental Science.”Journal of Economic
Perspectives 24 (2): 59–68.
– Nevo, A. and M. D. Whinston (2010). “Taking the Dogma out of Econometrics: Structural
Modeling and Credible Inference.” Journal of Economic Perspectives 24 (2): 69–82.
– Stock, J. H. (2010). “The Other Transformation in Econometric Practice: Robust Tools
for Inference.” Journal of Economic Perspectives 24 (2): 83–94.
Accommodations for Students with Disabilities
“If you have a disability for which you are or may be requesting accommodations, please contact
both your instructor and the O¢ ce of Academic Support Services, Williams Hall, Suite 301 (610-7584152) as early as possible in the semester. You must have documentation from the Academic Support
Services o¢ ce before accommodations can be granted.”
The Principles of Our Equitable Community
“Lehigh University endorses The Principles of Our Equitable Community [http://www.lehigh.edu/
~inprv/initiatives/PrinciplesEquity_Sheet_v2_032212.pdf]. We expect each member of this class to
acknowledge and practice these Principles. Respect for each other and for di¤ering viewpoints is a
vital component of the learning environment inside and outside the classroom.”
6
08/12/2015 version
Lecture Schedule and Assignments
Date
Week 1
8/25
8/27
Week 2
9/1
9/3
Week 3
9/8
9/10
Week 4
9/15
9/17
Week 5
9/22
9/24
Week 6
9/29
10/1
Week 7
10/6
10/8
Week 8
10/13
10/15
Week 9
10/20
10/22
Week 10
10/27
10/29
Week 11
11/3
11/5
Week 12
11/10
11/12
Week 13
11/17
11/19
Week 14
11/24
11/26
Week 15
12/1
12/3
Topics
Overview
Microeconometrics overview
Health econometrics
Basics of treatment effect analysis
Basics
The experimental ideal
Regression analysis
Fundametals
Heterogeneity and nonlinearity
Regression analysis
Regression details
Omitted variables and measurement errors
Nonparametric regression
Basics
Basics
Simulation and numerical optimization
Simulation
Numerical optimization
Selection on observables
Controlling for covariates
Inverse probability weighting
Selection on observables
Pacing break
Propensity score matching
Instrumental variables
IV and causality
Asymptotic 2SLS inference
Instrumental variables
Local average treatment effects
Details
Regression discontinuity design
Basics
Examples
Linear panel models
Differnece-in-differences
Fixed effect vs. lagged dependent variables
Linear panel models: extensions
Panel IV estimation
Panel IV estimation
Linear panel models: extensions
Hausman-Taylor, Arellano-Bond estimators
Thanksgiving break
Treatment effect analysis using Stata
To be determined
To be determined
Readings
Assignments
MHE 1, Handout
Handout
PS#1 due 9/1
MMA 25.1-25.2, Handout
MHE 2
PS#2 due 9/8
MHE 3.1-3.2, MMA 4.1-4.5
MHE 3.3, MMA 24.5
PS#3 due 9/15
MHE 3.4-3.5, MMA 11
MMA 4.7, Handout
PS#4 due 9/22
MMA 9, Handout
MUS 2.6, Handout
PS#5 due 9/29
MUS 4
MMA 10, MUS 11, Handout
PS#6 due 10/6
MMA 25.3, Handout
Handout
MMA 25.4, Handout
PS#7 due 10/27
MHE 4.1, MMA 4.8-4.9
MHE 4.2-4.3, MUS 6
MHE 4.4-4.5, MMA 25.7
MHE 4.6-4.7
PS#8 due 11/3
MHE 6, MMA 25.6
Handout
PS#9 due 11/17
MHE 5.1-5.2, MMA 25.5
MHE 5.3-5.4, MMA 21
MUS 9.1-9.2, MMA 22
MUS 9.1-9.2, MMA 22
PS#10 due 12/1
MUS 9.3-9.4, MMA 22
7
08/12/2015 version