rdpower: Power Calculations for RD Designs

In rdpower: Power Calculations for RD Designs

Power Calculations for RD Designs

Description

rdpower() performs power calculations for RD designs.

Usage

rdpower(
  data = NULL,
  cutoff = 0,
  tau = NULL,
  alpha = 0.05,
  nsamples = NULL,
  sampsi = NULL,
  samph = NULL,
  all = FALSE,
  bias = NULL,
  variance = NULL,
  plot = FALSE,
  graph.range = NULL,
  covs = NULL,
  covs_drop = TRUE,
  deriv = 0,
  p = 1,
  q = NULL,
  h = NULL,
  b = NULL,
  rho = NULL,
  kernel = "triangular",
  bwselect = "mserd",
  vce = "nn",
  cluster = NULL,
  scalepar = 1,
  scaleregul = 1,
  fuzzy = NULL,
  level = 95,
  weights = NULL,
  masspoints = "adjust",
  bwcheck = NULL,
  bwrestrict = TRUE,
  stdvars = FALSE
)

Arguments

data	a matrix (Y,R) containing the outcome variable and the running variable (as column vectors).
cutoff	the RD cutoff (default is 0).
tau	specifies the treatment effect under the alternative at which the power function is evaluated. The default is half the standard deviation of the outcome for the untreated group.
alpha	specifies the significance level for the power function. Default is 0.05.
nsamples	sets the total sample size to the left, sample size to the left inside the bandwidth, total sample size to the right and sample size to the right of the cutoff inside the bandwidth to calculate the variance when the running variable is not specified. When not specified, the values are calculated using the running variable.
sampsi	sets the sample size at each side of the cutoff for power calculation. The first number is the sample size to the left of the cutoff and the second number is the sample size to the right. Default values are the sample sizes inside the chosen bandwidth.
samph	sets the bandwidths at each side of the cutoff for power calculation. The first number is the bandwidth to the left of the cutoff and the second number is the bandwidth to the right. Default values are the bandwidths used by rdrobust.
all	displays the power using the conventional variance estimator, in addition to the robust bias corrected one.
bias	set bias to the left and right of the cutoff. If not specified, the biases are estimated using rdrobust.
variance	set variance to the left and right of the cutoff. If not specified, the variances are estimated using rdrobust.
plot	plots the power function using the conventional and robust bias corrected standard errors from rdrobust.
graph.range	range of the plot.
covs	option for rdrobust(): specifies additional covariates to be used for estimation and inference.
covs_drop	option for rdrobust(): if TRUE, it checks for collinear additional covariates and drops them. Default is TRUE.
deriv	option for rdrobust(): specifies the order of the derivative of the regression functions to be estimated.
p	option for rdrobust(): specifies the order of the local-polynomial used to construct the point-estimator.
q	option for rdrobust(): specifies the order of the local-polynomial used to construct the bias-correction.
h	option for rdrobust(): specifies the values of the main bandwidth to be used on the left and on the right of the cutoff, respectively.
b	option for rdrobust(): specifies the values of the bias bandwidth $b$ to be used on the left and on the right of the cutoff, respectively.
rho	option for rdrobust(): specifies the value of rho so that the bias bandwidth b equals b=h/rho.
kernel	option for rdrobust(): kernel function used to construct the local-polynomial estimators.
bwselect	option for rdrobust(): specifies the bandwidth selection procedure to be used.
vce	option for rdrobust(): specifies the procedure used to compute the variance-covariance matrix estimator.
cluster	option for rdrobust(): indicates the cluster ID variable used for the cluster-robust variance estimation with degrees-of-freedom weights.
scalepar	option for rdrobust(): specifies scaling factor for RD parameter of interest.
scaleregul	option for rdrobust(): specifies scaling factor for the regularization terms of bandwidth selectors.
fuzzy	option for rdrobust(): specifies the treatment status variable used to implement fuzzy RD estimation.
level	option for rdrobust(): sets the confidence level for confidence intervals.
weights	option for rdrobust(): is the variable used for optional weighting of the estimation procedure. The unit-specific weights multiply the kernel function.
masspoints	option for rdrobust(): checks and controls for repeated observations in tue running variable.
bwcheck	option for rdrobust(): if a positive integer is provided, the preliminary bandwidth used in the calculations is enlarged so that at least bwcheck unique observations are used.
bwrestrict	option for rdrobust(): if TRUE, computed bandwidths are restricted to lie withing the range of x. Default is bwrestrict=TRUE.
stdvars	option for rdrobust(): if TRUE, x and y are standardized before computing the bandwidths. Default is stdvars=TRUE.

Value

power.rbc	power against tau using robust bias corrected standard error
se.rbc	robust bias corrected standard error
sampsi.r	number of observations inside the window to the right of the cutoff
sampsi.l	number of observations inside the window to the left of the cutoff
samph.r	bandwidth to the right of the cutoff
samph.l	bandwidth to the left of the cutoff
alpha	significance level used in power function
tau	treatment effect under alternative hypothesis
bias.r	bias to the right of the cutoff
bias.l	bias to the left of the cutoff
Vr.rb	Robust bias corrected variance to the right of the cutoff
Vl.rb	Robust bias corrected variance to the left of the cutoff
N.r	Total sample size to the right of the cutoff
N.l	Total sample size to the left of the cutoff
power.conv	power against tau using conventional inference
se.conv	conventional standard error

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. (2019). Power Calculations for Regression Discontinuity Designs. Stata Journal, 19(1): 210-245.

Examples

# Toy dataset
X <- array(rnorm(2000),dim=c(1000,2))
R <- X[,1] + X[,2] + rnorm(1000)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(1000)
# Power against tau = 1
tmp <- rdpower(data=cbind(Y,R),tau=1)
# Power against tau = 1 including covariates
tmp <- rdpower(data=cbind(Y,R),tau=1,covs=X)

rdpower documentation built on June 22, 2022, 1:06 a.m.