rdmc {rdmulti}

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)