rdms: Point estimation and robust bias-corrected inference for...

In rdmulti: Robust Local Polynomial Methods for RD Designs with Multiple Cutoffs or Multiple Scores

Point estimation and robust bias-corrected inference for multi-score designs

Description

rdms() implements point estimation and robust bias-corrected inference for Regression Discontinuity (RD) designs with multiple scores, including cumulative-cutoff designs and designs with two running variables.

Usage

rdms(
  Y,
  X,
  C,
  X2 = NULL,
  zvar = NULL,
  C2 = NULL,
  rangemat = NULL,
  xnorm = NULL,
  fuzzy = NULL,
  derivvec = NULL,
  pooled_opt = NULL,
  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,
  ginv.tolvec = NULL,
  sharpbwvec = NULL,
  level = 95,
  plot = FALSE,
  conventional = FALSE,
  subset = NULL,
  data = NULL
)

Arguments

Y	outcome variable.
X	running variable.
C	vector of cutoffs.
X2	if specified, second running variable.
zvar	if X2 is specified, treatment indicator.
C2	if specified, second vector of cutoffs.
rangemat	matrix of cutoff-specific ranges for the running variable.
xnorm	normalized running variable to estimate pooled effect.
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.
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 rdplot() for details.
covs_list	list of 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.
ginv.tolvec	vector of cutoff-specific tolerances for generalized inverse calculations. See rdrobust() for details.
sharpbwvec	vector indicating whether fuzzy RD bandwidth selection uses the sharp RD model at each cutoff. See rdrobust() for details.
level	confidence level for confidence intervals. See rdrobust() for details.
plot	plots cutoff-specific and pooled estimates.
conventional	reports conventional, instead of robust-bias corrected, p-values and confidence intervals.
subset	optional subset of observations to use.
data	optional data frame for resolving string variable names.

Value

B	vector of bias-corrected coefficients
V	variance-covariance matrix of the estimators
Coefs	vector of conventional coefficients
Nh	vector of sample sizes within bandwidth at each cutoff
CI	bias corrected confidence intervals
H	bandwidth used at each cutoff
Pv	vector of robust p-values

Author(s)

Matias D. 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: cumulative cutoffs
X <- runif(1000,0,100)
C <- c(33,66)
Y <- (1+X)*(X<C[1])+(0.8+0.8*X)*(X>=C[1]&X<C[2])+(1.2+1.2*X)*(X>=C[2]) + rnorm(1000)
# rmds: basic syntax
tmp <- rdms(Y,X,C)

rdmulti documentation built on May 18, 2026, 1:07 a.m.