The package rdpower implements power, sample size, and minimum detectable effect calculations for Regression Discontinuity (RD) designs using local polynomial methods.
rdpower: ex-post power calculations for RD treatment effects.rdsampsi: required sample size calculations for target power.rdmde: minimum detectable effect calculations.
To install/update in Python type:
pip install rdpower
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Help: PYPI repository.
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Replication: rdpower illustration, senate data.
To install/update in R type:
install.packages('rdpower')
When the latest rdrobust is available on GitHub before CRAN, install it first:
install.packages('remotes')
remotes::install_github('rdpackages/rdrobust', subdir = 'R/rdrobust')
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Help: R Manual, CRAN repository.
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Examples/data: rdpower illustration, senate data.
To install/update in Stata type:
net install rdrobust, from(https://raw.githubusercontent.com/rdpackages/rdrobust/main/stata) replace
net install rdpower, from(https://raw.githubusercontent.com/rdpackages/rdpower/main/stata) replace
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Replication: do-file, senate data.
For overviews and introductions, see the rdpackages website.
- Cattaneo, Titiunik and Vazquez-Bare (2019): Power Calculations for Regression Discontinuity Designs. Stata Journal 19(1): 210-245.
- Calonico, Cattaneo and Titiunik (2014): Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326. Supplemental Appendix.
- Calonico, Cattaneo, Farrell and Titiunik (2019): Regression Discontinuity Designs Using Covariates. Review of Economics and Statistics 101(3): 442-451. Supplemental Appendix.
- Calonico, Cattaneo and Farrell (2020): Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs. Econometrics Journal 23(2): 192-210. Supplemental Appendix.
This work was supported in part by the National Science Foundation through grant SES-1357561.