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Topics in Microeconometrics (ECO-AD-MICMETRIC)

ECO-AD-MICMETRIC

Description

COURSE OUTLINE
This is an intensive course on topics of microeconometrics with five meetings of two hours. We will cover the basic theory and recent advances behind two popular econometric methods utilized by the modern empirical microeconomist: regression discontinuity and bunching.
 

COURSE PROGRAM:

• Nonparametric Statistics
o Kernel Density Estimator
o Local Polynomial Regression Estimator
o Bias and Variance Expansions
o Interior vs. Boundary Points
o Asymptotic Normality
o Mean Squared Error Optimal Bandwidths

• Regression Discontinuity Designs
o External Validity
o Multiple Thresholds
o Uniform Inference
o Permutation Inference
o School Matching Data

• Bunching Estimators
o Trapezoidal Approximation
o Partial Identification
o Tobit Regressions
o Censored Quantile Regressions
o STATA Command “bunching”

GRADING

The final grade will be an average of two problem sets (40%), a research proposal (40%), and participation during the course (20%).There will be two problem sets with theoretical and computational problems. The first problem set will be due on May 8th, and the second will be due on May 22th.
A written research proposal will be due on May 29th. I am looking for new ideas in econometrics on how to improve the way economists analyse data. The paper must have a literature review, a clear and formal description of the new method, and Monte Carlo simulations. Students may work alone or in groups of two. The research proposal should be a short paper no more than 10 pages long after you exclude figures, diagrams, tables, bibliography, and appendices. Formatting should be one and half spaced text, font size 12, and justified text. Grammar and neatness are being evaluated as well. Please include bibliographic references. You will be handing in the paper in PDF, the code that generates the simulations (data files if applicable), and formatted output produced by the code.

 

BIBLIOGRAPHY
• Nonparametric Statistics
o J. Fan and I. Gijbels (1996). “Local Polynomial Modelling and Its Applications:
Monographs on Statistics and Applied Probability.” Taylor & Francis, 1996.
o Ming-Yen Cheng, Jianqing Fan, and James S Marron (1997). On automatic
boundary corrections. The Annals of Statistics, 25(4):1691–1708, 1997
o Qi Li and Jeffrey Scott Racine (2007). Nonparametric econometrics: theory and
practice. Princeton University Press, 2007.
o B. Hansen (2022). Probability and Statistics for Economists. Princeton University
Press, 2022.
o B. Hansen (2022). Econometrics. Princeton University Press, 2022.
• Regression Discontinuity Designs
o J. Hahn, P. Todd, and W. Van der Klaauw (2001). Identification and estimation of
treatment effects with a regression-discontinuity design. Econometrica, 69(1):201–
209, 2001.
o Guido W Imbens and Thomas Lemieux (2008). Regression discontinuity designs: A
guide to practice. Journal of Econometrics, 142(2):615–635, 2008
o J. McCrary (2008). Manipulation of the running variable in the regression
discontinuity design: A density test. Journal of Econometrics, 142(2):698–714, 2008
o Guido Imbens and Karthik Kalyanaraman (2012). Optimal bandwidth choice for the
regression discontinuity estimator. The Review of Economic Studies, 79(3):933–
959, 2012
o Sebastian Calonico, Matias D Cattaneo, and Rocio Titiunik (2014). Robust
nonparametric confidence intervals for regression-discontinuity designs.
Econometrica, 82(6):2295–2326, 2014
o Sebastian Calonico, Matias D Cattaneo, and Rocio Titiunik (2015). Optimal datadriven
regression discontinuity plots. Journal of the American Statistical
Association, 110(512):1753–1769, 2015
o Yingying Dong (2018). Alternative assumptions to identify late in fuzzy regression
discontinuity designs. Oxford Bulletin of Economics and Statistics, 80(5):1020–
1027, 2018
o Michal Kolesár and Christoph Rothe (2018). Inference in regression discontinuity
designs with a discrete running variable. American Economic Review, 108(8):2277–
2304, 2018
o Ivan A Canay and Vishal Kamat (2018). Approximate permutation tests and
induced order statistics in the regression discontinuity design. The Review of
Economic Studies, 85(3):1577–1608, 2018
o Timothy B Armstrong and Michal Kolesár (2018). Optimal inference in a class of
regression models. Econometrica, 86(2):655–683, 2018
o Bertanha, M. (2020). “Regression discontinuity design with many thresholds”. Journal
of Econometrics, 218(1), 216-241.
o Timothy B Armstrong and Michal Kolesár (2020). Simple and honest confidence
intervals in nonparametric regression. Quantitative Economics, 11(1):1–39, 2020
3
o Bertanha, Imbens (2020). “External validity in fuzzy regression discontinuity designs.”
Journal of Business & Economic Statistics, 38(3):593–612
o Bertanha, Moreira (2020). “Impossible inference in econometrics: Theory and
applications.” Journal of Econometrics, 218(2):247–270.
o Federico A Bugni and Ivan A Canay. Testing continuity of a density via g-order
statistics in the regression discontinuity design (2021). Journal of Econometrics,
221(1):138–159, 2021
o Bartalotti, Bertanha, Calonico (2021). “Regression discontinuity designs in policy
evaluation”. In Handbook of Research Methods and Applications in Empirical
Microeconomics. Chapter 12, pg 325-358. Edward Elgar Publishing.
o Bertanha, Chung, (2023). “Permutation tests at nonparametric rates.” Journal of the
American Statistical Association, 118(544), 2833-2846.
o Bertanha, Luflade, Mourifie (2025). “Causal Effects in Matching Mechanisms with
Strategically Reported Preferences.” Working Paper arXiv 2307.14282
• Bunching Estimators
o Victor Chernozhukov and Han Hong (2002). Three-step censored quantile regression
and extramarital affairs. Journal of the American Statistical Association, 97(459):872–
882, 2002.
o Emmanuel Saez (2010). Do Taxpayers Bunch at Kink Points? American Economic
Journal: Economic Policy, 2(3):180–212, August 2010.
o Raj Chetty, John N. Friedman, Tore Olsen, and Luigi Pistaferri (2011). Adjustment
costs, firm responses, and micro vs. macro labor supply elasticities: Evidence from
Danish tax records. The Quarterly Journal of Economics, 126(2):749–804, 2011.
o Sören Blomquist and Whitney Newey (2017). The bunching estimator cannot identify
the taxable income elasticity. Technical report, NBER, September 2017.
o Felipe Goncalves and Steven Mello (2021). A few bad apples? racial bias in policing.
American Economic Review, 111(5):1406–1441, 2021.
o Daniel M Hungerman and Mark Ottoni-Wilhelm (2021). Impure impact giving: Theory
and evidence. Journal of Political Economy, 129(5):1553–1614, 2021.br />o Bertanha, McCallum, Seegert (2023). “Better bunching, nicer notching.” Journal of
Econometrics, 237(2), 105512.
o Bertanha, McCallum, Payne, Seegert, (2022). “Bunching estimation of elasticities
using Stata.” The Stata Journal, 22(3), 597-624.
o Bertanha, Caetano, Jales, Seegert (2024) "Bunching estimation methods." Handbook
of Labor, Human Resources and Population Economics.

CONTACT
Instructor: Prof. Marinho Bertanha
Email: [email protected]
Office: TBA
Office hours: by appointment via email.

ENROL FOR THIS COURSE