A Permutation Test for the Regression Kink Design

By P. Ganong, Simon Jäger. 2018

100 citations recorded by the source corpus.

ABSTRACT The regression kink (RK) design is an increasingly popular empirical method for estimating causal effects of policies, such as the effect of unemployment benefits on unemployment duration. Using simulation studies based on data from existing RK designs, we empirically document that the statistical significance of RK estimators based on conventional standard errors can be spurious. In the simulations, false positives arise as a consequence of nonlinearities in the underlying relationship between the outcome and the assignment variable, confirming concerns about the misspecification bias of discontinuity estimators pointed out by Calonico, Cattaneo, and Titiunik. As a complement to standard RK inference, we propose that researchers construct a distribution of placebo estimates in regions with and without a policy kink and use this distribution to gauge statistical significance. Under the assumption that the location of the kink point is random, this permutation test has exact size in finite samples for testing a sharp null hypothesis of no effect of the policy on the outcome. We implement simulation studies based on existing RK applications that estimate the effect of unemployment benefits on unemployment duration and show that our permutation test as well as inference procedures proposed by Calonico, Cattaneo, and Titiunik improve upon the size of standard approaches, while having sufficient power to detect an effect of unemployment benefits on unemployment duration. Supplementary materials for this article are available online.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6fd0b10a8ca9000cb790d091cf7ec8708ee3561d

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Citations in the corpus

Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs (2018, methodology): “(2014) showed that the interval estimator remains valid under a wider set of bandwidth sequences, even when minimal additional smoothness of the unknown regression functions is assumed, and it was found to perform much better than other methods in both simulations and replication studies (Ganong and”
Simple Local Polynomial Density Estimators (2018, methodology): “For introductions to RD designs, and further references, see Imbens and Lemieux (2008), Lee and Lemieux (2010), and Cattaneo, Titiunik, and Vazquez-Bare (2017). For recent papers on modern RD methodology see, for example, Arai and Ichimura (2018), Ganong and Jäger (2018), Hyytinen, Meriläinen, Saari”
A Practical Introduction to Regression Discontinuity Designs (2019, methodology): “Ganong and Jäger (2018) develop a permutation inference approach based on the idea of placebo RD cutoffs for the Kink RD designs, Regression Kink designs, and related settings.”
Regression Discontinuity Designs (2021, unknown): “Keele, Titiunik, and Zubizarreta (2015) consider Multi-Score and Geographic RD designs, Li, Mattei, and Mealli (2015) analyze principal stratification in a fuzzy RD setup, Ganong and Jäger (2018) study kink RD designs, and Cattaneo, Keele, Titiunik, and Vazquez-Bare (2016, 2021) investigate Multi-Cu”