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Regression Discontinuity Designs with Clustered Data: Variance and Bandwidth Choice

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  • Bartalotti, Otávio C.
  • Brummet, Quentin O.

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 relax this assumption, we derive the properties of traditional estimators in a setting that incorporates clustering at the level of the running variable, and propose an accompanying optimal-MSE bandwidth selection rule. Simulation results demonstrate that falsely assuming data are i.i.d. may lead to higher MSE due to inadequate bandwidth choice. We apply our procedure to analyze the impact of Low-Income Housing Tax Credits on neighborhood characteristics and low-income housing supply.

Suggested Citation

  • Bartalotti, Otávio C. & Brummet, Quentin O., 2016. "Regression Discontinuity Designs with Clustered Data: Variance and Bandwidth Choice," Staff General Research Papers Archive 3393, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:3393
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    1. 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.

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