rdpower 2.0.0

Power calculations, sample size calculations, and minimum detectable effect calculations for Regression Discontinuity designs.

RDPOWER

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.

See references for methodological and practical details.

Website: https://rdpackages.github.io/.

Source code: https://github.com/rdpackages/rdpower.

Authors

Matias D. Cattaneo ([e-mail])

Ricardo Masini ([e-mail])

Rocio Titiunik ([e-mail])

Gonzalo Vazquez-Bare ([e-mail])

Installation

To install/update use pip:

pip install rdpower

Usage

import pandas as pd

from rdpower import rdmde, rdpower, rdsampsi

data = pd.read_csv("rdpower_senate.csv")
z = data[["demvoteshfor2", "demmv"]]

power = rdpower(data=z, tau=5)
sample_size = rdsampsi(data=z, tau=5)
mde = rdmde(data=z)

• Replication: rdpower illustration, senate data.

Dependencies

• numpy
• pandas
• scipy
• rdrobust
• matplotlib

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.