Nothing Special   »   [go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-905320170000038017.html
   My bibliography  Save this book chapter

Regression Discontinuity Designs with Clustered Data

In: Regression Discontinuity Designs

Author

Listed:
  • Otávio Bartalotti
  • Quentin Brummet
Abstract
Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To fill this gap, we derive the properties of traditional local polynomial estimators in a fixed-Gsetting that allows for cluster dependence in the error term. Simulation results demonstrate that accounting for clustering in the data while selecting bandwidths may lead to lower MSE while maintaining proper coverage. We then apply our cluster-robust procedure to an application examining the impact of Low-Income Housing Tax Credits on neighborhood characteristics and low-income housing supply.

Suggested Citation

  • 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.
  • Handle: RePEc:eme:aecozz:s0731-905320170000038017
    DOI: 10.1108/S0731-905320170000038017
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320170000038017/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320170000038017/full/epub?utm_source=repec&utm_medium=feed&utm_campaign=repec&title=10.1108/S0731-905320170000038017
    Download Restriction: no

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320170000038017/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no

    File URL: https://libkey.io/10.1108/S0731-905320170000038017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Ke-Li, 2017. "Regression discontinuity with categorical outcomes," Journal of Econometrics, Elsevier, vol. 201(1), pages 1-18.
    2. Bartalotti Otávio, 2019. "Regression Discontinuity and Heteroskedasticity Robust Standard Errors: Evidence from a Fixed-Bandwidth Approximation," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-26, January.
    3. Steven Dieterle & Otávio Bartalotti & Quentin Brummet, 2020. "Revisiting the Effects of Unemployment Insurance Extensions on Unemployment: A Measurement-Error-Corrected Regression Discontinuity Approach," American Economic Journal: Economic Policy, American Economic Association, vol. 12(2), pages 84-114, May.
    4. Keita, Sekou & Mandon, Pierre, 2018. "Give a fish or teach fishing? Partisan affiliation of U.S. governors and the poverty status of immigrants," European Journal of Political Economy, Elsevier, vol. 55(C), pages 65-96.
    5. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    6. Sebastian Calonico & Matias D Cattaneo & Max H Farrell, 2020. "Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 192-210.
    7. 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.
    8. Matias D. Cattaneo & Rocío Titiunik, 2022. "Regression Discontinuity Designs," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 821-851, August.
    9. Ari Hyytinen & Jaakko Meriläinen & Tuukka Saarimaa & Otto Toivanen & Janne Tukiainen, 2018. "When does regression discontinuity design work? Evidence from random election outcomes," Quantitative Economics, Econometric Society, vol. 9(2), pages 1019-1051, July.
    10. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2021. "Covariate Adjustment in Regression Discontinuity Designs," Papers 2110.08410, arXiv.org, revised Aug 2022.
    11. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    12. Samuel Lordemus, 2022. "Does Aid for Malaria Increase with Exposure to Malaria Risk? Evidence from Mining Sites in the D.R.Congo," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 719-748, August.
    13. de Gendre, Alexandra & Lynch, John & Meunier, Aurélie & Pilkington, Rhiannon & Schurer, Stefanie, 2021. "Child Health and Parental Responses to an Unconditional Cash Transfer at Birth," IZA Discussion Papers 14693, Institute of Labor Economics (IZA).
    14. Nicholas A. Bowman & Nayoung Jang, 2022. "What is the Purpose of Academic Probation? Its Substantial Negative Effects on Four-Year Graduation," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(8), pages 1285-1311, December.
    15. Yang Lixiong, 2019. "Regression discontinuity designs with unknown state-dependent discontinuity points: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-18, April.

    More about this item

    Keywords

    Regression discontinuity designs; local polynomials; clustering; optimal bandwidth selection; C13; C14; C21;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eme:aecozz:s0731-905320170000038017. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.