期刊
JOURNAL OF ECONOMETRICS
卷 218, 期 1, 页码 216-241出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2019.09.010
关键词
Regression discontinuity; Multiple cutoffs; Average treatment effect; Peer-effects
资金
- B.F. Haley and E.S. Shaw Fellowship at SIEPR-Stanford, United States
- COREUcLouvain, Belgium
- ISLA-Notre Dame, United States
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 consistent and asymptotically normal estimator for such ATEs when heterogeneity follows a nonparametric function of cutoff characteristics in the sharp case. The proposed estimator converges at the minimax optimal rate of root-n for a specific choice of tuning parameters. 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 maximize its precision. (C) 2020 Elsevier B.V. All rights reserved.
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