rdmc: Analysis of RD designs with multiple cutoffs

In rdmulti: Analysis of RD Designs with Multiple Cutoffs or Scores

Analysis of RD designs with multiple cutoffs

Description

rdmc() analyzes RD designs with multiple cutoffs.

Usage

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
)

Arguments

Y	outcome variable.
X	running variable.
C	cutoff variable.
fuzzy	specifies a fuzzy design. See rdrobust() for details.
derivvec	vector of cutoff-specific order of derivatives. See rdrobust() for details.
pooled_opt	options to be passed to rdrobust() to calculate pooled estimand.
verbose	displays the output from rdrobust for estimating the pooled estimand.
pvec	vector of cutoff-specific polynomial orders. See rdrobust() for details.
qvec	vector of cutoff-specific polynomial orders for bias estimation. See rdrobust() for details.
hmat	matrix of cutoff-specific bandwidths. See rdrobust() for details.
bmat	matrix of cutoff-specific bandwidths for bias estimation. See rdrobust() for details.
rhovec	vector of cutoff-specific values of rho. See rdrobust() for details.
covs_mat	matrix of covariates. See rdrobust() for details.
covs_list	list of covariates to be used in each cutoff.
covs_dropvec	vector indicating whether collinear covariates should be dropped at each cutoff. See rdrobust() for details.
kernelvec	vector of cutoff-specific kernels. See rdrobust() for details.
weightsvec	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.
bwselectvec	vector of cutoff-specific bandwidth selection methods. See rdrobust() for details.
scaleparvec	vector of cutoff-specific scale parameters. See rdrobust() for details.
scaleregulvec	vector of cutoff-specific scale regularization parameters. See rdrobust() for details.
masspointsvec	vector indicating how to handle repeated values at each cutoff. See rdrobust() for details.
bwcheckvec	vector indicating the value of bwcheck at each cutoff. See rdrobust() for details.
bwrestrictvec	vector indicating whether computed bandwidths are restricted to the range or runvar at each cutoff. See rdrobust() for details.
stdvarsvec	vector indicating whether variables are standardized at each cutoff. See rdrobust() for details.
vcevec	vector of cutoff-specific variance-covariance estimation methods. See rdrobust() for details.
nnmatchvec	vector of cutoff-specific nearest neighbors for variance estimation. See rdrobust() for details.
cluster	cluster ID variable. See rdrobust() for details.
level	confidence level for confidence intervals. See rdrobust() for details.
plot	plots cutoff-specific estimates and weights.
conventional	reports conventional, instead of robust-bias corrected, p-values and confidence intervals.

Value

tau	pooled estimate
se.rb	robust bias corrected standard error for pooled estimate
pv.rb	robust bias corrected p-value for pooled estimate
ci.rb.l	left limit of robust bias corrected CI for pooled estimate
ci.rb.r	right limit of robust bias corrected CI for pooled estimate
hl	bandwidth to the left of the cutoff for pooled estimate
hr	bandwidth to the right of the cutofffor pooled estimate
Nhl	sample size within bandwidth to the left of the cutoff for pooled estimate
Nhr	sample size within bandwidth to the right of the cutoff for pooled estimate
B	vector of bias-corrected estimates
V	vector of robust variances of the estimates
Coefs	vector of conventional estimates
W	vector of weights for each cutoff-specific estimate
Nh	vector of sample sizes within bandwidth
CI	robust bias-corrected confidence intervals
H	matrix of bandwidths
Pv	vector of robust p-values
rdrobust.results	results from rdrobust for pooled estimate
cfail	Cutoffs where rdrobust() encountered problems

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. (2020). Analysis of Regression Discontinuity Designs with Multiple Cutoffs or Multiple Scores. Stata Journal, forthcoming.

Examples

# 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)

rdmulti documentation built on July 9, 2023, 5:49 p.m.