rddapp (version 1.2.1)

mfrd_est: Multivariate Frontier Regression Discontinuity Estimation

Description

mfrd_est implements the frontier approach in Wong, Steiner and Cook (2013). It is based on the MFRDD code in Stata.

Usage

mfrd_est(
  y,
  x1,
  x2,
  c1,
  c2,
  t.design = NULL,
  local = 0.15,
  front.bw = NA,
  m = 10,
  k = 5,
  kernel = "triangular",
  ngrid = 250,
  margin = 0.03,
  boot = NULL,
  cluster = NULL,
  stop.on.error = TRUE
)

Arguments

y

The outcome variable (continuous).

x1

The assignment variable 1.

x2

The assignment variable 2.

c1

The cutoff of assignment variable 1.

c2

The cutoff of assignment variable 2.

t.design

The treatment option according to design. The 1st entry is for x1: "g" means treatment is assigned if x1 is greater than its cutoff, "geq" means treatment is assigned if x1 is greater than or equal to its cutoff, "l" means treatment is assigned if x1 is less than its cutoff, "leq" means treatment is assigned if x1 is less than or equal to its cutoff. The 2nd entry is for x2.

local

The range of neighboring points around the cutoff on the standardized scale on each assignment variable, which is a positive number.

front.bw

A numeric vector specifying the bandwidths at which to estimate the RD for each of three effects models. If NA, front.bw will be determined by cross validation.

m

The number of uniformly-at-random samples to draw as search candidates for front.bw if not given.

k

An integer specifying the number of folds for cross validation to determine front.bw if not given.

kernel

A string specifying the kernel to be used in the local linear fitting. "triangular" kernel is the default. Other options are "rectangular", "epanechnikov", "quartic", "triweight", "tricube", "gaussian" and "cosine".

ngrid

The number of non-zero grid points on each assignment variable, which is also the number of zero grid points on each assignment variable. Value used in Wong, Steiner and Cook (2013) is 2500, which may cause long computational time.

margin

The range of grid points beyond the minimum and maximum of sample points on each assignment variable.

boot

The number of bootstrap samples to obtain standard error of estimates.

cluster

An optional vector specifying clusters within which the errors are assumed to be correlated. This will result in reporting cluster robust SEs. This option overrides anything specified in se.type. It is suggested that data with a discrete running variable be clustered by each unique value of the running variable (Lee and Card, 2008).

stop.on.error

Logical. If TRUE (the default), removes bootstraps which cause error in the integrate function, and resample till the specified number of bootstrap samples are acquired.

Value

mfrd_est returns an object of class "mfrd".

Examples

# NOT RUN {
set.seed(12345)
x1 <- runif(1000, -1, 1)
x2 <- runif(1000, -1, 1)
cov <- rnorm(1000)
y <- 3 + 2 * (x1 >= 0) + 3 * cov + 10 * (x2 >= 0) + rnorm(1000)
mfrd_est(y = y, x1 = x1, x2 = x2, c1 = 0, c2 = 0, t.design = c("geq", "geq"))
# }