rdrobust 2.0.0

Implements local polynomial Regression Discontinuity (RD) point estimators with robust bias-corrected confidence intervals and inference procedures.

RDROBUST

The package rdrobust implements estimation, inference, and graphical procedures for Regression Discontinuity (RD) designs using local polynomial methods.

• rdrobust: point estimation and robust bias-corrected inference.
• rdbwselect: data-driven bandwidth selection for RD estimation and inference.
• rdplot: data-driven RD plots based on binned means and local polynomial fits.

See references for methodological and practical details.

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

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

Authors

Sebastian Calonico ([e-mail])

Matias D. Cattaneo ([e-mail])

Max H. Farrell ([e-mail])

Ricardo Masini ([e-mail])

Rocio Titiunik ([e-mail])

Installation

To install/update use pip:

pip install rdrobust

Usage

from rdrobust import rdrobust, rdbwselect, rdplot, plot_rdrobust, rdrobust_RDsenate

# Load bundled Senate dataset (1390 x 14)
df = rdrobust_RDsenate()

# Sharp RD with HC3 heteroskedasticity-robust variance
r = rdrobust(y=df['vote'], x=df['margin'], vce='hc3')

# Cluster-robust variance (CRV3, Pustejovsky-Tipton 2018)
r = rdrobust(y=df['vote'], x=df['margin'], cluster=df['state'], vce='cr3')

# Bandwidth selection
bw = rdbwselect(y=df['vote'], x=df['margin'], all=True)

# Diagnostic plot of an rdrobust object, with effect panel
fig = plot_rdrobust(r, y=df['vote'], x=df['margin'], show_effect=True)

• Replication: rdrobust illustration, rdplot illustration, senate data.

Dependencies

• numpy
• pandas
• scipy
• plotnine
• matplotlib

References

For overviews and introductions, see rdpackages website.

Software and Implementation

• Calonico, Cattaneo and Titiunik (2014): Robust Data-Driven Inference in the Regression-Discontinuity Design.
  Stata Journal 14(4): 909-946.

• Calonico, Cattaneo and Titiunik (2015): rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs.
  R Journal 7(1): 38-51.

• Calonico, Cattaneo, Farrell and Titiunik (2017): rdrobust: Software for Regression Discontinuity Designs.
  Stata Journal 17(2): 372-404.

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 and Titiunik (2015): Optimal Data-Driven Regression Discontinuity Plots.
  Journal of the American Statistical Association 110(512): 1753-1769.
  Supplemental Appendix.

• Calonico, Cattaneo and Farrell (2018): On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference.
  Journal of the American Statistical Association 113(522): 767-779.
  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.

• Calonico, Cattaneo and Farrell (2022): Coverage Error Optimal Confidence Intervals for Local Polynomial Regression.
  Bernoulli 28(4): 2998-3022.
  Supplemental Appendix.