Regression Discontinuity Designs with Clustered Data

Bartalotti, Otávio; Brummet, Quentin

doi:10.1108/s0731-905320170000038017

Cited by 29 publications

(19 citation statements)

References 14 publications

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“…We therefore also report cluster-robust confidence intervals (but acknowledge that the choice of the clustering unit is hard to justify). See Bartalotti and Brummet (2017) for a recent analysis of cluster-based inference in the context of RDD. as determined by IK and CCT implementations of the MSE-optimal bandwidth for the linear specification.…”

Section: Rdd Estimations: Baseline Resultsmentioning

confidence: 99%

When does regression discontinuity design work? Evidence from random election outcomes

Hyytinen¹,

Meriläinen²,

Saarimaa³

et al. 2018

Quantitative Economics

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We use elections data in which a large number of ties in vote counts between candidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non‐experimental regression discontinuity design (RDD) estimates against the estimate produced by this experiment that takes place exactly at the cutoff. The experimental estimate suggests that there is no personal incumbency advantage. In contrast, conventional local polynomial RDD estimates suggest a moderate and statistically significant effect. Bias‐corrected RDD estimates that apply robust inference are, however, in line with the experimental estimate. Therefore, state‐of‐the‐art implementation of RDD can meet the replication standard in the context of close elections.

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Section: Rdd Estimations: Baseline Resultsmentioning

confidence: 99%

When does regression discontinuity design work? Evidence from random election outcomes

Hyytinen¹,

Meriläinen²,

Saarimaa³

et al. 2018

Quantitative Economics

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“…The latter case being of interest in, for example, Card, Lee, Pei andWeber (2015, 2017), Dong and Lewbel (2015), Cerulli, Dong, Lewbel and Poulsen (2017), and Ganong and Jäger (2018). We also discuss extensions to fuzzy, geographic, multi-score, and multi-cutoff RD designs (Hahn, Todd and van der Klaauw, 2001;Papay, Willett and Murnane, 2011;Keele and Titiunik, 2015;Cattaneo, Keele, Titiunik and Vazquez-Bare, 2016), as well as to clustered data and/or inclusion of pre-intervention covariates (Lee and Card, 2008;Bartalotti and Brummet, 2017;Calonico, Cattaneo, Farrell and Titiunik, 2018c). Our results can also be applied to other RD settings such as those considered in Xu (2017), Dong (2018), and Dong, Lee and Gou (2018).…”

Section: Introductionmentioning

confidence: 91%

“…When the data exhibits clustering, first-order asymptotic results can be easily extended to account for clustered sampling where (i) each unit i belongs to exactly one of G clusters and (ii) G → ∞ and Gh → ∞ (see Bartalotti and Brummet (2017) and Calonico, Cattaneo, Farrell and Titiunik (2018c)). Since MSE-optimal bandwidth choices in this context are available and fully implemented, the corresponding ROT implementation is:…”

Section: Clustered Datamentioning

confidence: 99%

Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

Calónico

Cattaneo

Farrell

2019

The Econometrics Journal

260

176

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Modern empirical work in Regression Discontinuity (RD) designs employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator must be shrank when the goal is to construct RBC confidence intervals with the smaller coverage error rate. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in R and Stata packages.

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“…Gh → ∞-see Cameron and Miller (2015) for a review of cluster-robust inference, and Bartalotti and Brummet (2017) for a discussion in the context of MSE-optimal bandwidth selection for sharp RD designs. This extension is conceptually straightforward but notationally cumbersome, and is deferred to the supplemental appendix.…”

Section: Asymptotic Distribution and Valid Inferencementioning

confidence: 99%

Regression Discontinuity Designs Using Covariates

Calónico

Cattaneo

Farrell

et al. 2019

The Review of Economics and Statistics

500

445

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We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions, and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. An empirical illustration and an extensive simulation study is presented. All methods are implemented in R and Stata software packages.

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Regression Discontinuity Designs with Clustered Data

Cited by 29 publications

References 14 publications

When does regression discontinuity design work? Evidence from random election outcomes

When does regression discontinuity design work? Evidence from random election outcomes

Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

Regression Discontinuity Designs Using Covariates

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