rdsensitivity: Sensitivity analysis for RD designs under local randomization

In rdlocrand: Local Randomization Methods for RD Designs

Sensitivity analysis for RD designs under local randomization

Description

rdsensitivity analyze the sensitivity of randomization p-values and confidence intervals to different window lengths.

Usage

rdsensitivity(
  Y,
  R,
  cutoff = 0,
  wlist,
  wlist_left,
  tlist,
  statistic = "diffmeans",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  fuzzy = NULL,
  ci = NULL,
  ci_alpha = 0.05,
  reps = 1000,
  seed = 666,
  nodraw = FALSE,
  quietly = FALSE
)

Arguments

Y	a vector containing the values of the outcome variable.
R	a vector containing the values of the running variable.
cutoff	the RD cutoff (default is 0).
wlist	the list of windows to the right of the cutoff. By default the program constructs 10 windows around the cutoffwith 5 observations each.
wlist_left	the list of windows to the left of the cutoff. If not specified, the windows are constructed symmetrically around the cutoff based on the values in wlist.
tlist	the list of values of the treatment effect under the null to be evaluated. By default the program employs ten evenly spaced points within the asymptotic confidence interval for a constant treatment effect in the smallest window to be used.
statistic	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.
p	the order of the polynomial for outcome adjustment model. 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.
fuzzy	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.
ci	returns the confidence interval corresponding to the indicated window length. ci has to be a two-dimensional vector indicating the left and right limits of the window. Default alpha is .05 (95% level CI).
ci_alpha	Specifies value of alpha for the confidence interval. Default alpha is .05 (95% level CI).
reps	number of replications. Default is 1000.
seed	the seed to be used for the randomization tests.
nodraw	suppresses contour plot.
quietly	suppresses the output table.

Value

tlist	treatment effects grid
wlist	window grid
results	table with corresponding p-values for each window and treatment effect pair.
ci	confidence interval (if ci is specified).

Author(s)

Matias Cattaneo, Princeton University. [e-mail]

Rocio Titiunik, Princeton University. [e-mail]

Gonzalo Vazquez-Bare, UC Santa Barbara. [e-mail]

References

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Examples

# Toy dataset
R <- runif(100,-1,1)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Sensitivity analysis
# Note: low number of replications to speed up process.
# The user should increase the number of replications.
tmp <- rdsensitivity(Y,R,wlist=seq(.75,2,by=.25),tlist=seq(0,5,by=1),reps=500)

rdlocrand documentation built on June 22, 2022, 1:05 a.m.