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Regression Discontinuity Design with Many Thresholds

Marinho Bertanha

arXiv 4 Jan 2021 · Econometrics · publishedJournal of Econometrics (2020) · 33 citations (OpenAlex)

arXiv:2101.01245 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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 non-parametric 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.

Citation extraction

48
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hahn, Todd and Van der Klaauw (2001) Identification and Estimation of Treatment Effects with a Regression-discontinuity Design1.00054100%
2Angrist and Lavy (1999) Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement0.87452100%
3Bajari, Hong, Park and Town (2017) Estimating Price Sensitivity of Economic Agents Using Discontinuity in Nonlinear Contracts0.84333100%
4De La Mata (2012) The Effect of Medicaid Eligibility on Coverage, Utilization, and Children's Health0.81142100%
5Pop-Eleches and Urquiola (2013) Going to a Better School: Effects and Behavioral Responses0.81142100%
6Porter (2003) Estimation in the Regression Discontinuity Model0.7374350%
7Calonico, Cattaneo and Farrell (2018) Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs0.64441100%
8Agarwal, Chomsisengphet, Mahoney and Stroebel (2017) Do Banks Pass Through Credit Expansions to Consumers Who Want to Borrow?0.64422100%
9Calonico, Cattaneo and Titiunik (2014) Robust Nonparametric Confidence Intervals for Regression-discontinuity Designs0.64422100%
10Cattaneo, Titiunik, Vazquez-Bare and Keele (2016) Interpreting Regression Discontinuity Designs with Multiple Cutoffs0.64422100%

Showing the top 10 of 48 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs0.64432
2Regression Discontinuity Designs0.64422
3Joint Inference for the Regression Discontinuity Effect and Its External Validity0.64422
4Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs0.40511
5Covariate Adjustment in Regression Discontinuity Designs0.40511
6Optimal Decision Rules Under Partial Identification0.40511
7Nonparametric Treatment Effect Identification in School Choice0.40511
8Causal Effects in Matching Mechanisms with Strategically Reported Preferences0.40511
9On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs0.40511