rddapp-package: Regression Discontinuity Design Application
In rddapp: Regression Discontinuity Design Application
rddapp-package R Documentation
Regression Discontinuity Design Application
Description
rddapp: A package for regression discontinuity designs (RDDs).
Details
The rddapp package provides a set of functions for the analysis
of the regression-discontinuity design (RDD). The three main parts are:
estimation of effects of interest, power analysis, and assumption checks.
Estimation
A variety of designs can be estimated in various ways.
The single-assignment RDD (both sharp and fuzzy) can be analyzed using
both a parametric (global) or non-parametric (local) approach.
The multiple-assignment RDD (both sharp and fuzzy) can be analyzed using both parametric and
non-parametric estimation. The analysis choices are further to use estimate effects based on
univariate scaling, the centering approach, or the frontier approach. The frontier approach
can currently only be estimated using parametric regression with bootstrapped standard errors.
Power analysis
Statistical power can be be estimated for both the single- and multiple-assignment RDD,
(both sharp and fuzzy), including all parametric and non-parametric estimators mentioned in
the estimation section. All power analyses are based on a simulation approach, which means that
the user has to provide all necessary parameters for a data-generating model.
Assumption checks
An important part of any RDD are checks of underlying assumptions. The package provides users
with the option to estimate McCrary's sorting test (to identify violations of assignment rules),
checks of discontinuities of other baseline covariates, along with sensitivity checks of the
chosen bandwidth parameter for non-parametric models, and so-called placebo tests, that examine
the treatment effect at other cut-points along the assignment variable.
Author(s)
Ze Jin zj58@cornell.edu,
Wang Liao wl483@cornell.edu,
Irena Papst ip98@cornell.edu,
Wenyu Zhang wz258@cornell.edu,
Kimberly Hochstedler kah343@cornell.edu,
Felix Thoemmes, fjt36@cornell.edu
rddapp documentation built on April 6, 2023, 1:15 a.m.
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| rddapp-package | R Documentation |
Regression Discontinuity Design Application
Description
rddapp: A package for regression discontinuity designs (RDDs).
Details
The rddapp package provides a set of functions for the analysis of the regression-discontinuity design (RDD). The three main parts are: estimation of effects of interest, power analysis, and assumption checks.
Estimation
A variety of designs can be estimated in various ways. The single-assignment RDD (both sharp and fuzzy) can be analyzed using both a parametric (global) or non-parametric (local) approach. The multiple-assignment RDD (both sharp and fuzzy) can be analyzed using both parametric and non-parametric estimation. The analysis choices are further to use estimate effects based on univariate scaling, the centering approach, or the frontier approach. The frontier approach can currently only be estimated using parametric regression with bootstrapped standard errors.
Power analysis
Statistical power can be be estimated for both the single- and multiple-assignment RDD, (both sharp and fuzzy), including all parametric and non-parametric estimators mentioned in the estimation section. All power analyses are based on a simulation approach, which means that the user has to provide all necessary parameters for a data-generating model.
Assumption checks
An important part of any RDD are checks of underlying assumptions. The package provides users with the option to estimate McCrary's sorting test (to identify violations of assignment rules), checks of discontinuities of other baseline covariates, along with sensitivity checks of the chosen bandwidth parameter for non-parametric models, and so-called placebo tests, that examine the treatment effect at other cut-points along the assignment variable.
Author(s)
Ze Jin zj58@cornell.edu, Wang Liao wl483@cornell.edu, Irena Papst ip98@cornell.edu, Wenyu Zhang wz258@cornell.edu, Kimberly Hochstedler kah343@cornell.edu, Felix Thoemmes, fjt36@cornell.edu
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