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Regression Discontinuity Designs Using Covariates

Author

Listed:
  • Sebastian Calonico

    (Columbia University)

  • Matias D. Cattaneo

    (Princeton University)

  • Max H. Farrell

    (University of Chicago)

  • Rocío Titiunik

    (Princeton University)

Abstract

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. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.

Suggested Citation

  • Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Rocío Titiunik, 2019. "Regression Discontinuity Designs Using Covariates," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 442-451, July.
  • Handle: RePEc:tpr:restat:v:101:y:2019:i:3:p:442-451
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    References listed on IDEAS

    as
    1. Jens Ludwig & Douglas L. Miller, 2007. "Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design," The Quarterly Journal of Economics, Oxford University Press, vol. 122(1), pages 159-208.
    2. Yoichi Arai & Hidehiko Ichimura, 2018. "Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator," Quantitative Economics, Econometric Society, vol. 9(1), pages 441-482, March.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. Jasjeet S. Sekhon & Rocío Titiunik, 2017. "On Interpreting the Regression Discontinuity Design as a Local Experiment," Advances in Econometrics, in: Regression Discontinuity Designs, volume 38, pages 1-28, Emerald Group Publishing Limited.
    5. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    6. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
    7. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    8. Otávio Bartalotti & Quentin Brummet, 2017. "Regression Discontinuity Designs with Clustered Data," Advances in Econometrics, in: Regression Discontinuity Designs, volume 38, pages 383-420, Emerald Group Publishing Limited.
    9. Matias D. Cattaneo & Juan Carlos Escanciano (ed.), 2017. "Regression Discontinuity Designs," Advances in Econometrics, Emerald Publishing Ltd, volume 38, number aeco.2017.38.
    10. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    11. Lee, David S., 2008. "Randomized experiments from non-random selection in U.S. House elections," Journal of Econometrics, Elsevier, vol. 142(2), pages 675-697, February.
    12. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Roc ́ıo Titiunik, 2017. "rdrobust: Software for regression-discontinuity designs," Stata Journal, StataCorp LP, vol. 17(2), pages 372-404, June.
    13. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, December.
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