rdlocrand: a Stata Package for Inference in Regression Discontinuity Designs under Local Randomizati
Gonzalo Vazquez-Bare (),
Matias Cattaneo () and
Rocio Titiunik ()
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Gonzalo Vazquez-Bare: Department of Economics, University of Michigan
2016 Stata Conference from Stata Users Group
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.
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