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14.385 syllabus
Massachusetts Institute of Technology
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MIT Fall Semester 2017
Department of Economics 14 Nonlinear Econometric Analysis
Class Meetings:
Lectures: Mon/Wed 1:00-2:30pm at E51- Recitations: Fri 1:00-2:30pm at E51-
Instructors:
First half: Whitney Newey, E52- Email: wnewey@mit Office hours: M, T: 4-5 pm
Second half: Alberto Abadie, E52- Email: abadie@mit Office hours: Mon. 2:30-4:00pm
Teaching Assistant: Benjamin Deaner Email: bdeaner@mit Office Hours: T: 4 pm.
Course Outline:
This course covers nonlinear econometric methods for cross-sectional and panel data, including various estimation methods, large sample theory, bootstrapping, many mo- ments and weak identification, partial identification, nonlinear panel data, discrete choice models, quantile regression, nonparametric and semiparametric estimation, and treat- ment effects. Methods are illustrated with economic applications.
Course Information:
Enrollment is limited. Grading will be based on problem sets. These will involve both theoretical calculations and computer exercises in which you will be asked to analyze data sets. You can use any computer package you wish. Solutions will be handed out written in Matlab or R. Problem sets will be due at the beginning of class, and in order to allow us to post the solutions quickly on the course’s web page we will not accept late problem sets. Students are allowed to collaborate in small groups (of no more than four students) for the assignments. Students in a group are allowed to share jointly writtencomputer code and to work together to solve the assignments. However, each student must write
up her or his answers completely independently. At the beginning of the code please writedownthenamesofallthepeoplewhohelpedcreateit.
Due Dates for Problem Sets: Firsthalf:Sep25,Oct16,Oct30.
Prerequisites:14 (or equivalent with the permission of the instructor).
Readings: The course material is self contained and there is no required textbook for the course. Handouts covering most of the material will be distributed in class or through the website. Some students mightfind it useful to have a textbook as an additional reference. Good reference books are:
Wooldridge, J. (2010), Econometric Analysis of Cross Section and Panel Data, second edition, MIT Press.
van der Vaart (1998), Asymptotic Statistics. Cambridge University Press.
Cameron, A. and Trivedi, P. (2005) Microeconometrics, Methods and Applications, Cam- bridge University Press.
This syllabus also includes a list of additional readings that are not essential for following the lectures, but useful for a deeper understanding of the material. Many of these are available electronically.
Code of Conduct:All course activities, including class meetings and homework as- signments are subject to MIT’s academic integrity policies as detailed at integrity.mit/. Please be on time and make sure that your cell phone is turned offduring class time.
Accommodations for Students with Disabilities: The Department of Eco- nomics values an inclusive environment. If you need a disability accommodation to access this course, please communicate with us early in the semester. If youhave your accommodation letter, please meet with the faculty so that we can understand your needs and implement your approved accommodations. Ifyouhavenotyetbeenapprovedforac- commodations, please contact Student Disability Services at uaap- sds@mit to learn about their procedures. We encourage you to do so early in the term to allow sufficient time for implementation of services/accommodations that you may need.
Laptop Policy:Laptops may be used in class.
Outline and Approximate Schedule for First Half of Course
- Estimation of Nonlinear Models(5 lectures): Maximum Likelihood, GMM, Minimum Distance, Extremum Estimators, Consistency, Asymptotic Normality, Infer- ence, Simulation Methods, Two Step Estimators, Indirect Inference.
*Notes Wooldridge (2010), Chapters 12—12, 13.1-13, 13, 14.1-14 (skim) Newey and McFadden (1994), Sections 1-5 (skim) Van der Vaart (1998), Chapters 5.1-5 (advanced).
- (1 lecture)
*Notes *Horowitz, J. (2001): ”The Bootstrap,”Handbook of Econometrics, Vol. 5.
- Bayesian and Quasi-Bayesian Methods(2 lectures):
*Notes *Chernozhukov, V. and H. Hong (2003): ”An MCMC approach to Classical estima- tion,”Journal of Econometrics115: 293-346. Cameron and Trivedi (2005), Chapters 12-
- Alternative Asymptotic Approximations.(2 lectures)
*Notes *Imbens, G. and J. Wooldridge (2007): ”Weak Instruments and Many Instruments,” Lecture Notes 13. Staiger, D. and J. Stock (1997): “Instrumental Variables Regression with Weak In- struments”,Econometrica65, 57-586. Kleibergen, F. (2002): ”Pivotal Statisticsfor Testing Structural Parameters in In- strumental Variables Regression,”Econometrica70, 1781-1803. Moreira, M. (2003): “A Conditional Likelihood Ratio Test for Structural Models,” Econometrica71, 1027-1048. *Andrews, D.W. and J. Stock (2007): ”Inference with Weak Instruments,” in Blundell, R., W. Newey, T. Persson eds.,Advances in Economics and Econometrics,Vol. 3. Bekker, P. (1994): ”Alternative Approximations to the Distributions of Instrumen- tal Variables Estimators,”Econometrica63, 657-681. Chao, J. and N. Swanson (2005): ”Consistent Estimation With a Large Number of Weak Instruments,”Econometrica73, 1673-692. *Hansen, C., J. Hausman, and W. Newey (2008): ”Estimation with Many In- strumental Variables,”Journal of Business and Economic Statistics,26, 398-422. Stock, J. and J. Wright (2000): ”GMM With Weak Identification,”Econometrica68, 1055-1096.
Kleibergen, F. (2005): ”Testing Parameters in GMM Without Assuming They are Identified,”Econometrica73, 1103-1123. Newey, W. and F. Windmeijer (2008): ”GMM Estimation with Many WeakMo- ment Conditions,”Econometrica77, 687-719. Hausman, J., W. Newey, T. Woutersen, J. Chao, and N. Swanson (2008): ”In- strumental Variable Estimation with Heteroskedasticity and Many Instruments,” working paper.
5 Identification(2 lectures).
*Notes *Haile, P. and E. Tamer (2003): ”Inference With An Incomplete Model of English Auctions,”Journal of Political Economy111: 1—51. *Tamer, E. (2003): “Incomplete Simultaneous Discrete Response Model with Multiple Equilibria,”Review of Economic Studies70, 147-167. *Chernozhukov, V., H. Hong, and E. Tamer (2007): ”Estimation and Confidence Regions for Parameter Sets in Econometric Models,”Econometrica75, 1243-1284. Beresteanu, A. and F. Molinari (2008): ”Asymptotic Properties for a Class of Partially Identified Models,”Econometrica76, 763-814.
- Nonlinear Panel Data(2 lectures).
*Newey, Lecture Notes. *Hahn, J. and W. Newey (2004): “Jacknife and Analytical Bias Reduction for Nonlinear Panel Data Models,”Econometrica72, 1295-1319. Wooldridge, Chapter 13. Chamberlain, G. (1984): “Panel Data,”Handbook of Econometrics, Vol. 2, Section 3. Manski, C. (1987): “Semiparametric Analysis of Random Effects Linear Models From Binary Response Data,”Econometrica55, 357-362. Honore, B. (1992): “Trimmed Lad and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects,”Econometrica60, 533- Lancaster, T. “The Incidental Parameter Problem Since 1948,”Journal of Economet- rics,2000, pp. 391- Arellano, M. and B. Honore, “Panel Data Models: Some Recent Developments,” Handbook of Econometrics, Vol. 5. Honore, B., and E. Tamer (2006): “Bounds on Parameters in Dynamic Discrete Choice Models,”Econometrica74, 611-629. Chamberlain, G. (2010): “Binary Response Models for Panel Data: Identification and Estimation,”Econometrica78, 159-168. Chernozhukov, V., I. Fernandez-Val, J. Hahn, and W. Newey (2013): “Average and Quantile Effects in Nonseparable Panel Models,”Econometrica81, 535-580. Chernozhukov, V, I. Fernandez-Val, S. Hoderlein, H. Holzmann, W. Newey (2015): ”Nonparametric Identification in Panels using Quantiles,”Journal of Econometrics.
Newey, W. (1994), “The Asymptotic Variance of Semiparametric Estimators,”Econo- metrica, vol. 62, 1349-1382.
Pagan, A. and A. Ullah (1999), Nonparametric Econometrics. Cambridge University Press.
9 Estimation of Treatment Effects
The following two readings are overviews of the material that we will cover in this section.
*Imbens, G. and J. Wooldridge (2009) “Recent Developmentsin the Econometrics of Program Evaluation,”Journal of Economic Literature, vol. 47(1), 5-86.
Angrist, J. and J. Pischke (2009), Mostly Harmless Econometrics: An Empiricist Companion. Princeton University Press.
9 Causality, Counterfactuals, and Potential Outcomes.
Imbens, G., and D. Rubin (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge UniversityPress. Chapter 1.
9 Randomized Experiments. Fisher’s Exact Test.
Bloom, H., L. L. Orr, S. Bell, G. Cave, F. Doolittle, W. Lin andJ. Bos (1997), “The Benefits and Costs of JTPA Title II-A Programs,”Journal of Human Resources, vol. 32, 549-576.
Duflo, E., R. Glennerster and M. Kremer (2008), “Using Randomization in Development Economics Research: A Toolkit,” in T. Schultz and J. Strauss eds. Handbook of Development Economics, vol. 4. Elsevier Science.
Krueger, A. (1999), “Experimental Estimates of Education Production Functions,”Quar- terly Journal of Economics, vol. 114, 497-532.
Imbens and Rubin: Chapter 5.
Rosenbaum, P. (2002), Observational Studies (second edition). Springer-Verlag. Chap- ter 2.
9 Introduction to Observational Studies
*Rosenbaum, P. (2009), Design of Observational Studies. Springer-Verlag. Chapter 1, sections 1-6.
9 Matching and Selection on Observables. Directed Acyclic Graphs.
*Imbens, G. (2004), “Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review,”Review of Economics and Statistics, vol. 86(1), 4-29.
Abadie, A. and G. Imbens (2006), “Large Sample Properties of Matching Estimators for Average Treatment Effects,”Econometrica,vol. 74, 235-267.
Abadie, A. and G. Imbens (2016), “Matching on the Estimated Propensity Score,” Econometrica,vol. 84, 781-807.
Dehejia, R. and S. Wahba (1999), “Causal Effects in Non-Experimental Studies: Re- Evaluating the Evaluation of Training Programs,”Journal of the American Statistical Association, vol. 94, 1053-1062.
Heckman, J., H. Ichimura and P. Todd (1997), “Matching as an Econometric Eval- uation Estimator: Evidence from Evaluating a Job Training Programme,”Review of Economic Studies, vol. 64, 605-654.
Imbens and Rubin: Chapters 12-18.
Hern ́an, M. and J. Robins (2016), Causal Inference. Available online at: hsph.harvard/miguel-hernan/causal-inference-book/
Pearl, J. (2009), Causality (second edition). Cambridge University Press.
Rosenbaum, P., and D. B. Rubin (1983), “The Central Role ofthe Propensity Score in Observational Studies for Causal Effects,”Biometrika, vol. 70, 41-55.
Rubin, D. (1977), “Assignment to Treatment Group on the Basis of a Covariate,” Journal of Educational Statistics, vol. 2, 1-26.
9 Robustness, Sensitivity, Falsification
Imbens, G. (2003), “Sensitivity to Exogeneity Assumptionsin Program Evaluation,” American Economic Review (Papers & Proceedings), vol. 93(2), 126-132.
Imbens and Rubin: Chapter 22.
9 Differences-in-Differences. Synthetic Controls.
*Abadie, A., A. Diamond and J. Hainmueller (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program,”Journal of the American Statistical Association, vol. 105, 493-505.
Abadie, A. and J. Gardeazabal (2003), “The Economic Costs of Conflict: A Case Study of the Basque Country,”American Economic Review, vol. 93(1), 113-132.
Card, D. (1990), “The Impact of the Mariel Boatlift on the Miami Labor Market,” Industrial and Labor Relations Review, vol. 44, 245-257.
9 The Regression Discontinuity Design
Lee, D, and T. Lemieux (2010), “Regression Discontinuity Designs in Economics,” Journal of Economic Literature, vol. 48, 281-355.
Calonico, S., M. Cattaneo, and R. Titiunik (2014), “Robust Nonparametric Bias- Corrected Inference in the Regression Discontinuity Design,”Econometrica, vol. 82, 2295-2326.
Imbens, G. and K. Kalyanaraman (2012), “Optimal Bandwidth Choice for the Regression Discontinuity Estimator,”Review of Economic Studies, vol. 79, 933-959.
Imbens, G. and T. Lemieux (2008), “Regression Discontinuity Designs: A Guide to Practice,”Journal of Econometrics, vol. 142, 615-635.
14.385 syllabus
University: Massachusetts Institute of Technology
