This short course (6 hours) provides an introduction to Regression Discontinuity Design (RDD). It covers core concepts, identification assumptions, estimation techniques, and practical applications of RDD. The course combines lectures with hands-on sessions using Stata, offering participants theoretical insights and practical experience. Sessions will be delivered online.
Assessment The assessment for this course is designed to evaluate both students’ theoretical understanding and their ability to practically apply Regression Discontinuity Design (RDD) methods. It consists of two main components:
Empirical Application: Students will undertake an empirical research project or a replication study where they apply RDD techniques to a real-world dataset. The project should comprehensively address the following: 1. A clear formulation of the research question and the assignment variable; 2. A thorough graphical analysis, including plots that visualize the relationship between the running variable and the outcome; 3. Estimation of treatment effects using RDD methods; 4. Conducting robustness checks, such as testing for bandwidth sensitivity and checking for manipulation in the assignment variable (e.g., McCrary density test); 5. A critical discussion of the results, acknowledging potential limitations, sources of bias, and the reliability of the estimates. The project will be assessed based on the rigour of the methodology, the clarity of presentation, and the correct implementation of RDD techniques.
• Research Note: Students are required to submit a written research note (approximately 2000 words) summarising their empirical findings and addressing the limitations of their analysis. This report should provide a concise, yet thorough, interpretation of the results, discussing both their statistical significance and their broader implications. Additionally, students must submit the Stata do-files used for the analysis along with the report.
References Angrist, J.D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press. [Chapter 6]
Reading list Angrist, J.D., & Rokkanen, M. (2015). Wanna get away? Regression discontinuity estimation of exam school effects away from the cutoff. Journal of the American Statistical Association, 110(512), 1331–1344
Calonico, S., Cattaneo, M.D., & Titiunik, R. (2014). Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica, 82(6), 2295–2326.
Imbens, G., & Kalyanaraman, K. (2012). Optimal bandwidth choice for the regression discontinuity estimator. The Review of Economic Studies, 79(3), 933–959.
Imbens, G.W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615–635.
Lee, D.S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281–355.
McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics, 142(2), 698–714.
