Including Covariates in the Regression Discontinuity Design
By Markus Frölich, Martin Huber. Journal of business & economic statistics, 2018
62 citations recorded by the source corpus.
This article proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one-dimensional nonparametric regression, irrespective of the dimension of the continuously distributed elements in the conditioning set. Furthermore, the proposed method may decrease bias and restore identification by controlling for discontinuities in the covariate distribution at the discontinuity threshold, provided that all relevant discontinuously distributed variables are controlled for. To illustrate the estimation approach and its properties, we provide a simulation study and an empirical application to an Austrian labor market reform. Supplementary materials for this article are available online.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2d685c1b808f03bd32b2add18af3c4c91d8d8fd9
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In Statistics and Probability
- M. D. Cattaneo
- R. Titiunik
- Max H. Farrell
- Sebastian Calonico
- Alberto Abadie
- Guido W. Imbens
- Parag A. Pathak
- Atila Abdulkadiroglu
Citations in the corpus
- Breaking Ties: Regression Discontinuity Design Meets Market Design (2019, background): “3See, among others, Frolich (2007); Cattaneo et al. (2015, 2017); Frandsen (2017); Sekhon and Titiunik (2017); Frolich and Huber (2019); and Arai et al. (2019). inference in a wide variety of applications.”
- Regression Discontinuity Designs Using Covariates (2018, methodology): “Second, Frölich and Huber (2018) develop a nonparametric kernel method to account for observed covariates for RD treatment effect estimation.”
- Regression Discontinuity Designs (2021, unknown): “, Ganong and Jäger, 2018; Hyytinen, Meriläinen, Saarimaa, Toivanen, and Tukiainen, 2018; De Magalhaes, Hangartner, Hirvonen, Meriläinen, Ruiz, and Tukiainen, 2020). The estimation and inference methods described so far extend naturally to fuzzy RD designs, where the local polynomial point estimator ”