Lehigh University Department of Economics
Muzhe Yang Fall 2017
ECONOMICS 464: APPLIED ECONOMETRICS (I) Instructor: Muzhe Yang Course Time: Tuesday and Thursday, 1:10–2:25 PM Course Location: Lewis Lab 512 Course Website: Course Site (https://coursesite.lehigh.edu/) Contact Information:
[email protected], (610) 758-4962 O¢ ce Hours: Tuesday and Thursday 3:00–4:30 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
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Lehigh University Department of Economics
Muzhe Yang Fall 2017
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. Regression analysis – MHE 3; MMA 4.1–4.5, 4.7, 11, 24.5; Handout – 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. 2
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Lehigh University Department of Economics
Muzhe Yang Fall 2017
– 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 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. – 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 3
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Lehigh University Department of Economics
Muzhe Yang Fall 2017
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. – 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. – Cattaneo, M. D., R. Titiunik and G. Vazquez-Bare (2017). “Comparing Inference Approaches for RD Designs: A Reexamination of the E¤ect of Head Start on Child Mortality.” Journal of Policy Analysis and Management 36 (3): 643–681. – 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. – 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 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. 4
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Lehigh University Department of Economics
Muzhe Yang Fall 2017
– 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. – 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. 5
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Lehigh University Department of Economics
Muzhe Yang Fall 2017
– 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 O¢ ce of Academic Support Services in the Dean of Students o¢ ce addresses requests for accommodations for learning and/or physical disabilities for undergraduate and graduate students. For more information, I encourage you to visit the web site at http://studenta¤airs.lehigh.edu/disabilities. 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.”
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Lecture Schedule and Assignments Date Week 1 8/29 8/31 Week 2 9/5 9/7 Week 3 9/12 9/14 Week 4 9/19 9/21 Week 5 9/26 9/28 Week 6 10/3 10/5 Week 7 10/10 10/12 Week 8 10/17 10/19 Week 9 10/24 10/26 Week 10 10/31 11/2 Week 11 11/7 11/9 Week 12 11/14 11/16 Week 13 11/21 11/23 Week 14 11/28 11/30 Week 15 12/5 12/7
Topics Basics of treatment effect analysis Microeconometrics overview Basics Basics of treatment effect analysis Student presentation on PS#1 The experimental ideal Regression analysis Student presentation on PS#2 Fundamentals Regression analysis Regression details Omitted variables and measurement errors Nonparametric regression Basics Basics Simulation and numerical optimization Simulation Numerical optimization Simulation and numerical optimization Numerical optimization Student presentation on PS#6 Selection on observables Pacing break Inverse probability weighting Selection on observables Propensity score matching Propensity score matching Instrumental variables Local average treatment effects Local average treatment effects Regression discontinuity (RD) design Student presentation on PS#8 RD Basics Difference-in-differences RD Basics Difference-in-differences Linear panel models: extensions Fixed effect vs. lagged dependent variables Thanksgiving break Linear panel models: extensions Panel IV estimation Hausman-Taylor, Arellano-Bond estimators Review To be determined To be determined
Readings
Assignments
MHE 1, Handout MMA 25.1-25.2, Handout
PS#1 due 9/5
MHE 2
PS#2 due 9/12
MHE 3.1-3.3, MMA 4.1-4.5
PS#3 due 9/21
MHE 3.4-3.5, MMA 11 MMA 4.7, Handout
PS#4 due 9/28
MMA 9, Handout MUS 2.6, Handout
PS#5 due 10/5
MUS 4 MMA 10, MUS 11, Handout
PS#6 due 10/12
MMA 10, MUS 11, Handout
MMA 25.3, Handout MMA 25.4, Handout MMA 25.4, Handout
PS#7 due 10/31
MHE 4.1-4.7, MMA 25.7, Handout MHE 4.1-4.7, MMA 25.7, Handout
PS#8 due 11/7
MHE 6, MMA 25.6
PS#9 due 11/21
MHE 6, MMA 25.6 MHE 5.1-5.2, MMA 25.5, Handout MHE 5.3-5.4
MUS 9.1-9.2, MMA 22 MUS 9.3-9.4, MMA 22
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PS#10 due 12/5
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