RDPOWER

The rdpower package provides Python, R, and Stata implementations of power, sample size, and minimum detectable effects calculations using robust bias-corrected local polynomial inference methods.

This work was supported by the National Science Foundation through grant SES-1357561.

Website

https://rdpackages.github.io/rdpower

Queries and Requests

Please email: rdpackages@googlegroups.com

Major Upgrades

This package was first released in Fall 2016, and had one major upgrade in Fall 2020.

• Fall 2020 new feature: command/function rdmde for computing minimum detectable effects.

Python Implementation

To install/update in Python type:

pip install rdpower

• Help: PYPI repository.

• Replication: py-script, senate data.

R Implementation

To install/update in R type:

install.packages('rdpower')

• Help: R Manual, CRAN repository.

• Replication files: R-script, data-senate.

Stata Implementation

To install/update in Stata type:

net install rdpower, from(https://raw.githubusercontent.com/rdpackages/rdpower/master/stata) replace

• Help: rdpower, rdsampsi, rdmde.

• Replication: do-file, data-senate.

References

For overviews and introductions, see rdpackages website.

Software and Implementation

• Cattaneo, Titiunik and Vazquez-Bare (2019): Power Calculations for Regression Discontinuity Designs.
  Stata Journal 19(1): 210-245.

Technical and Methodological

• 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.