The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Inference in Regression Discontinuity Designs with a Discrete Running Variable
Open accessAbstract
We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values. In particular, we study the common practice of using confidence intervals (CIs) based on standard errors that are clustered by the running variable as a means to make inference robust to model misspecification (Lee and Card 2008). We derive theoretical results and present simulation and empirical evidence showing that these CIs do not guard against model misspecification, and that they have poor coverage properties. We therefore recommend against using these CIs in practice. We instead propose two alternative CIs with guaranteed coverage properties under easily interpretable restrictions on the conditional expectation function. (JEL C13, C51, J13, J31, J64, J65)
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
A Practitioner’s Guide to Cluster-Robust Inference
Colin Cameron, A., & Miller, D. L. (2015) · Journal of Human Resources · doi:10.3368/jhr.50.2.317 · Cited by 5,037
Regression discontinuity designs in economics
Lee, D. S., & Lemieux, T. (2010) · Journal of Economic Literature · doi:10.1257/jel.48.2.281 · Cited by 4,534
Robust nonparametric confidence intervals for regression-discontinuity designs
Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014) · Econometrica · doi:10.3982/ecta11757 · Cited by 3,036
Large sample properties of matching estimators for average treatment effects
Abadie, A., & Imbens, G. W. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00655.x · Cited by 2,908
Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design
Hahn, J., Todd, P., & Klaauw, W. (2001) · Econometrica · doi:10.1111/1468-0262.00183 · Cited by 2,679
MacKinnon, J. G., & White, H. (1985) · Journal of Econometrics · doi:10.1016/0304-4076(85)90158-7 · Cited by 1,879
Increasing Residual Wage Inequality: Composition Effects, Noisy Data, or Rising Demand for Skill?
Lemieux, T. (2006) · American Economic Review · doi:10.1257/aer.96.3.461 · Cited by 952
Regression discontinuity inference with specification error
Lee, D. S., & Card, D. (2008) · Journal of Econometrics · doi:10.1016/j.jeconom.2007.05.003 · Cited by 853
Oreopoulos, P. (2006) · American Economic Review · doi:10.1257/000282806776157641 · Cited by 813
Research works printed but not linked: 2
- Bell Robert M, 2002, Survey Methodology, 28, 169
- Oreopoulos Philip, 2008, American Economic Review. https://assets.aeaweb.org/assets/production/articles-attachments/aer/contents/corrigenda/corr_ aer., 96, 152, 10.1257/000282806776157641 · DOI · Paper page
Cited by
A More Credible Approach to Parallel Trends
Rambachan, A., & Roth, J. (2023) · Review of Economic Studies · doi:10.1093/restud/rdad018 · Cited by 1,559
When Is Parallel Trends Sensitive to Functional Form?
Roth, J., & Sant'Anna, P. H. C. (2023) · Econometrica · doi:10.3982/ecta19402 · Cited by 142
A day late and a dollar short: Liquidity and household formation among student borrowers
Goodman, S., Isen, A., & Yannelis, C. (2021) · Journal of Financial Economics · doi:10.1016/j.jfineco.2021.04.033 · Cited by 55
Inference with a single treated cluster
Hagemann, A. (2020) · arXiv · doi:10.48550/arxiv.2010.04076 · Cited by 18
Kikuta, K., & Uesugi, M. (2023) · International Organization · doi:10.1017/s0020818322000261 · Cited by 17
Metadata
- Authors
- Kolesár, M., & Rothe, C.
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2018-08-01
- Volume
- 108
- Issue
- 8
- Pages
- 2277-2304
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W3102524825
- Cited by
- 269
- References
- 29
- FWCI
- 14.51
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Inference in Regression Discontinuity Designs with a Discrete Running Variable. https://app.cochat.ai/papers/10.1257/aer.20160945
- The paper
- Kolesár, M., & Rothe, C. (2018). Inference in Regression Discontinuity Designs with a Discrete Running Variable. American Economic Review. https://doi.org/10.1257/aer.20160945
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
