rdmc()
analyzes RD designs with multiple cutoffs.
rdmc(
Y,
X,
C,
fuzzy = NULL,
derivvec = NULL,
pooled_opt = NULL,
verbose = FALSE,
pvec = NULL,
qvec = NULL,
hmat = NULL,
bmat = NULL,
rhovec = NULL,
covs_mat = NULL,
covs_list = NULL,
covs_dropvec = NULL,
kernelvec = NULL,
weightsvec = NULL,
bwselectvec = NULL,
scaleparvec = NULL,
scaleregulvec = NULL,
masspointsvec = NULL,
bwcheckvec = NULL,
bwrestrictvec = NULL,
stdvarsvec = NULL,
vcevec = NULL,
nnmatchvec = NULL,
cluster = NULL,
level = 95,
plot = FALSE,
conventional = FALSE
)
pooled estimate
robust bias corrected standard error for pooled estimate
robust bias corrected p-value for pooled estimate
left limit of robust bias corrected CI for pooled estimate
right limit of robust bias corrected CI for pooled estimate
bandwidth to the left of the cutoff for pooled estimate
bandwidth to the right of the cutofffor pooled estimate
sample size within bandwidth to the left of the cutoff for pooled estimate
sample size within bandwidth to the right of the cutoff for pooled estimate
vector of bias-corrected estimates
vector of robust variances of the estimates
vector of conventional estimates
vector of weights for each cutoff-specific estimate
vector of sample sizes within bandwidth
robust bias-corrected confidence intervals
matrix of bandwidths
vector of robust p-values
results from rdrobust for pooled estimate
Cutoffs where rdrobust() encountered problems
outcome variable.
running variable.
cutoff variable.
specifies a fuzzy design. See rdrobust()
for details.
vector of cutoff-specific order of derivatives. See
rdrobust()
for details.
options to be passed to rdrobust()
to calculate
pooled estimand.
displays the output from rdrobust
for estimating the
pooled estimand.
vector of cutoff-specific polynomial orders. See
rdrobust()
for details.
vector of cutoff-specific polynomial orders for bias estimation.
See rdrobust()
for details.
matrix of cutoff-specific bandwidths. See rdrobust()
for
details.
matrix of cutoff-specific bandwidths for bias estimation. See
rdrobust()
for details.
vector of cutoff-specific values of rho. See rdrobust()
for details.
matrix of covariates. See rdrobust()
for details.
list of covariates to be used in each cutoff.
vector indicating whether collinear covariates should be
dropped at each cutoff. See rdrobust()
for details.
vector of cutoff-specific kernels. See rdrobust()
for
details.
vector of length equal to the number of cutoffs indicating
the names of the variables to be used as weights in each cutoff. See rdrobust()
for details.
vector of cutoff-specific bandwidth selection methods. See
rdrobust()
for details.
vector of cutoff-specific scale parameters. See
rdrobust()
for details.
vector of cutoff-specific scale regularization
parameters. See rdrobust()
for details.
vector indicating how to handle repeated values at each
cutoff. See rdrobust()
for details.
vector indicating the value of bwcheck at each cutoff. See
rdrobust()
for details.
vector indicating whether computed bandwidths are
restricted to the range or runvar at each cutoff. See rdrobust()
for
details.
vector indicating whether variables are standardized at
each cutoff. See rdrobust()
for details.
vector of cutoff-specific variance-covariance estimation
methods. See rdrobust()
for details.
vector of cutoff-specific nearest neighbors for variance
estimation. See rdrobust()
for details.
cluster ID variable. See rdrobust()
for details.
confidence level for confidence intervals. See rdrobust()
for details.
plots cutoff-specific estimates and weights.
reports conventional, instead of robust-bias corrected, p-values and confidence intervals.
Matias Cattaneo, Princeton University. cattaneo@princeton.edu
Rocio Titiunik, Princeton University. titiunik@princeton.edu
Gonzalo Vazquez-Bare, UC Santa Barbara. gvazquez@econ.ucsb.edu
Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2020). Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores. Stata Journal, forthcoming.
# Toy dataset
X <- runif(1000,0,100)
C <- c(rep(33,500),rep(66,500))
Y <- (1 + X + (X>=C))*(C==33)+(.5 + .5*X + .8*(X>=C))*(C==66) + rnorm(1000)
# rdmc with standard syntax
tmp <- rdmc(Y,X,C)
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