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RDHonest (version 1.0.1)

Honest Inference in Regression Discontinuity Designs

Description

Honest and nearly-optimal confidence intervals in fuzzy and sharp regression discontinuity designs and for inference at a point based on local linear regression. The implementation is based on Armstrong and Kolesár (2018) , and Kolesár and Rothe (2018) . Supports covariates, clustering, and weighting.

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Install

install.packages('RDHonest')

Monthly Downloads

239

Version

1.0.1

License

GPL-3

Issues

0

Pull Requests

0

Stars

61

Forks

15

Maintainer

Michal Koles<c3><a1>r

Last Published

December 16th, 2024

Functions in RDHonest (1.0.1)

rcp

Battistin, Brugiavini, Rettore, and Weber (2009) retirement consumption puzzle dataset
RDTEfficiencyBound

Finite-sample efficiency bounds for minimax CIs
RDHonestBME

Honest CIs in sharp RD with discrete regressors under BME function class
RDScatter

Scatterplot of binned raw observations
RDHonest

Honest inference in RD
RDSmoothnessBound

Lower bound on smoothness constant M in sharp RD designs
CVb

Critical values for CIs based on a biased Gaussian estimator.
rebp

Austrian unemployment duration data from Lalive (2008)
lee08

Lee (2008) US House elections dataset
headst

Head Start data from Ludwig and Miller (2007)
cghs

Oreopoulos (2006) UK general household survey dataset