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rdlocrand: a Stata Package for Inference in Regression Discontinuity Designs under Local Randomizati


  • Gonzalo Vazquez-Bare

    (Department of Economics, University of Michigan)

  • Matias Cattaneo

    (Department of Economics and Department of Statistics, University of Michigan)

  • Rocio Titiunik

    (Department of Political Science, University of Michigan)


We introduce the Stata module rdlocrand, which contains four commands to conduct finite-sample inference in regression discontinuity (RD) designs under a local randomization assumption, following the framework and methods proposed in Cattaneo, Frandsen, and Titiunik (2015) and Cattaneo, Titiunik, and Vazquez-Bare (2016). Assuming a known assignment mechanism for units close to the RD cutoff, these functions implement a variety of procedures based on randomization inference techniques. First, the command rdrandinf employs randomization methods to conduct point estimation, hypothesis testing and confidence interval estimation under different assumptions. Second, the command rdwinselect employs finite-sample methods to select a window near the cutoff where the assumption of randomized treatment assignment is most plausible. Third, the command rdsensitivity employs randomization techniques to conduct a sequence of hypothesis tests for different windows around the RD cutoff, which can be used to assess the sensitivity of the methods as well as to construct confidence intervals by inversion. Finally, the command rdrbounds implements sensitivity bounds (Rosenbaum 2002) for the context of RD designs under local randomization. Companion R functions with the same syntax and capabilities are also provided.

Suggested Citation

  • Gonzalo Vazquez-Bare & Matias Cattaneo & Rocio Titiunik, 2016. "rdlocrand: a Stata Package for Inference in Regression Discontinuity Designs under Local Randomizati," 2016 Stata Conference 25, Stata Users Group.
  • Handle: RePEc:boc:scon16:25

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