# rdwinselect

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##### Window selection for RD designs under local randomization

rdwinselect implements the window-selection procedure based on balance tests for RD designs under local randomization. Specifically, it constructs a sequence of nested windows around the RD cutoff and reports binomial tests for the running variable runvar and covariate balance tests for covariates covariates (if specified). The recommended window is the largest window around the cutoff such that the minimum p-value of the balance test is larger than a prespecified level for all nested (smaller) windows. By default, the p-values are calculated using randomization inference methods.

##### Usage
rdwinselect(
R,
X,
cutoff = 0,
obsmin = NULL,
wmin = NULL,
wobs = NULL,
wstep = NULL,
wmasspoints = FALSE,
nwindows = 10,
statistic = "diffmeans",
p = 0,
evalat = "cutoff",
kernel = "uniform",
approx = FALSE,
level = 0.15,
reps = 1000,
seed = 666,
plot = FALSE,
quietly = FALSE,
obsstep = NULL
)
##### Arguments
R

a vector containing the values of the running variable.

X

the matrix of covariates to be used in the balancing tests. The matrix is optional but the recommended window is only provided when at least one covariate is specified. This should be a matrix of size n x k where n is the total sample size and $k$ is the number of covariates.

cutoff

the RD cutoff (default is 0).

obsmin

the minimum number of observations above and below the cutoff in the smallest window. Default is 10.

wmin

the smallest window to be used.

wobs

the number of observations to be added at each side of the cutoff at each step. Default is 5

wstep

the increment in window length.

wmasspoints

specifies that the running variable is discrete and each masspoint should be used as a window.

nwindows

the number of windows to be used. Default is 10.

statistic

the statistic to be used in the balance tests. Allowed options are diffmeans (difference in means statistic), ksmirnov (Kolmogorov-Smirnov statistic), ranksum (Wilcoxon-Mann-Whitney standardized statistic) and hotelling (Hotelling's T-squared statistic). Default option is diffmeans. The statistic ttest is equivalent to diffmeans and included for backward compatibility.

p

the order of the polynomial for outcome adjustment model (for covariates). Default is 0.

evalat

specifies the point at which the adjusted variable is evaluated. Allowed options are cutoff and means. Default is cutoff.

kernel

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.

approx

forces the command to conduct the covariate balance tests using a large-sample approximation instead of finite-sample exact randomization inference methods.

level

the minimum accepted value of the p-value from the covariate balance tests. Default is .15.

reps

number of replications. Default is 1000.

seed

the seed to be used for the randomization tests.

plot

draws a scatter plot of the minimum p-value from the covariate balance test against window length.

quietly

suppress output

obsstep

the minimum number of observations to be added on each side of the cutoff for the sequence of fixed-increment nested windows. This option is deprecated and only included for backward compatibility.

##### Value

window

recommended window (NA is covariates are not specified)

wlist

list of window lengths

results

table including window lengths, minimum p-value in each window, corresponding number of the variable with minimum p-value (i.e. column of covariate matrix), Binomial test p-value and sample sizes to the left and right of the cutoff in each window.

summary

summary statistics.

##### References

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.

• rdwinselect
##### Examples
# NOT RUN {
# Toy dataset
X <- array(rnorm(200),dim=c(100,2))
R <- X[1,] + X[2,] + rnorm(100)
# Window selection adding 5 observations at each step
# Note: low number of replications to speed up process.
tmp <- rdwinselect(R,X,obsmin=10,wobs=5,reps=500)
# Window selection setting initial window and step
# The user should increase the number of replications.
tmp <- rdwinselect(R,X,wmin=.5,wstep=.125,reps=500)
# Window selection with approximate (large sample) inference and p-value plot
tmp <- rdwinselect(R,X,wmin=.5,wstep=.125,approx=TRUE,nwin=80,quietly=TRUE,plot=TRUE)

# }

Documentation reproduced from package rdlocrand, version 0.7, License: GPL-2

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