Joint Econometrics and Statistics Workshop
Flexible Covariate Adjustments in Regression Discontinuity Designs
Claudia Noack (University of Oxford)
Friday 25 March 2022 12:00 - 13:00
Many of our seminars and public events this year will continue as in person or as hybrid (online and in person) events. Please check our website listings and Twitter feed @STICERD_LSE for updates.
Unless otherwise specified, in-person seminars are open to the public.
Those unable to join the seminars in-person are welcome to participate via zoom if the event is hybrid.
About this event
Empirical regression discontinuity (RD) studies often use covariates to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than the linear adjustment estimators that are currently used widely in practice. Our approach can accommodate a possibly large number of either discrete or continuous covariates. It involves running a standard RD analysis with an appropriately modified outcome variable, which takes the form of the difference between the original outcome and a function of the covariates. We characterize the function that leads to the estimator with the smallest asymptotic variance, and show how it can be estimated via modern machine learning, nonparametric regression, or classical parametric methods. The resulting estimator is easy to implement because tuning parameters can be chosen as in a conventional RD analysis. An extensive simulation study illustrates the performance of our approach. This is joint work with Tomasz Olma and Christoph Rothe. Please register to attend: https://lse.zoom.us/j/81317393418
Econometrics and Statistics seminars are held on Fridays in term time at 12:00-13:00, ONLINE, unless specified otherwise.
Seminar organisers: Dr Tatiana Komarova and Dr Yunxiao Chen.
For further information please contact Lubala Chibwe: email@example.com.
Please use this link to subscribe or unsubscribe to the Econometrics and Statistics seminars mailing list (stats).