Regression Discontinuity Design with Many Thresholds
No 2016026, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)
Numerous empirical studies employ regression discontinuity designs with multiple cutoffs and heterogeneous treatments. A common practice is to normalize all the cutoffs to zero and estimate one effect. This procedure identifies the average treatment effect (ATE) on the observed distribution of individuals local to existing cutoffs. However, researchers often want to make inferences on more meaningful ATEs computed over general counterfactual distributions of individuals rather than simply the observed distribution of individuals local to existing cutoffs. This paper proposes a root-n consistent and asymptotically normal estimator for such ATEs when heterogeneity follows a non-parametric function of cutoff characteristics in the sharp case. It shows that identification in the fuzzy case with multiple cutoffs is impossible unless heterogeneity follows a finite dimensional function of cutoff characteristics. Under parametric heterogeneity, this paper proposes an ATE estimator for the fuzzy case that optimally combines observations to minimize its mean squared error.
Keywords: Regression Discontinuity Designs; Multiple Cutoffs; Average Treat- ment Effect; Alternative Asymptotics; Peer-effects (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 I21 (search for similar items in EconPapers)
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Working Paper: Regression Discontinuity Design with Many Thresholds (2021)
Journal Article: Regression discontinuity design with many thresholds (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2016026
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