rdrbounds
calculates lower and upper bounds for the
randomization p-value under different degrees of departure from a
local randomized experiment, as suggested by Rosenbaum (2002).
rdrbounds(
Y,
R,
cutoff = 0,
wlist,
gamma,
expgamma,
bound = "both",
statistic = "ranksum",
p = 0,
evalat = "cutoff",
kernel = "uniform",
fuzzy = NULL,
nulltau = 0,
prob,
fmpval = FALSE,
reps = 1000,
seed = 666
)
a vector containing the values of the outcome variable.
a vector containing the values of the running variable.
the RD cutoff (default is 0).
the list of window lengths to be evaluated. By default the program constructs 10 windows around the cutoff, the first one including 10 treated and control observations and adding 5 observations to each group in subsequent windows.
the list of values of gamma to be evaluated.
the list of values of exp(gamma) to be evaluated. Default is c(1.5,2,2.5,3)
.
specifies which bounds the command calculates. Options are upper
for upper bound, lower
for lower bound and both
for both upper and lower bounds. Default is both
.
the statistic to be used in the balance tests. Allowed options are diffmeans
(difference in means statistic), ksmirnov
(Kolmogorov-Smirnov statistic) and ranksum
(Wilcoxon-Mann-Whitney standardized statistic). Default option is diffmeans
. The statistic ttest
is equivalent to diffmeans
and included for backward compatibility.
the order of the polynomial for outcome adjustment model. Default is 0.
specifies the point at which the adjusted variable is evaluated. Allowed options are cutoff
and means
. Default is cutoff
.
specifies the type of kernel to use as weighting scheme. Allowed kernel types are uniform
(uniform kernel), triangular
(triangular kernel) and epan
(Epanechnikov kernel). Default is uniform
.
indicates that the RD design is fuzzy. fuzzy
can be specified as a vector containing the values of the endogenous treatment variable, or as a list where the first element is the vector of endogenous treatment values and the second element is a string containing the name of the statistic to be used. Allowed statistics are ar
(Anderson-Rubin statistic) and tsls
(2SLS statistic). Default statistic is ar
. The tsls
statistic relies on large-sample approximation.
the value of the treatment effect under the null hypothesis. Default is 0.
the probabilities of treatment for each unit when assignment mechanism is a Bernoulli trial. This option should be specified as a vector of length equal to the length of the outcome and running variables.
reports the p-value under fixed margins randomization, in addition to the p-value under Bernoulli trials.
number of replications. Default is 1000.
the seed to be used for the randomization tests.
list of gamma values.
list of exp(gamma) values.
window grid.
p-values for each window (under gamma = 0).
list of lower bound p-values for each window and gamma pair.
list of upper bound p-values for each window and gamma pair.
M.D. Cattaneo, B. Frandsen and R. Titiunik. (2015). Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate. Journal of Causal Inference 3(1): 1-24.
M.D. Cattaneo, R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.
M.D. Cattaneo, R. Titiunik and G. Vazquez-Bare. (2017). Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality. Journal of Policy Analysis and Management 36(3): 643-681.
P. Rosenbaum (2002). Observational Studies. Springer.
# NOT RUN {
# Toy dataset
R <- runif(100,-1,1)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Rosenbaum bounds
# Note: low number of replications and windows to speed up process.
# The user should increase these values.
rdrbounds(Y,R,expgamma=c(1.5,2),wlist=c(.3),reps=100)
# }
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