RDOptBW.fit: Optimal bandwidth selection in RD

Description Usage Arguments Value Examples

View source: R/RD_lp.R

Description

Basic computing engine called by RDOptBW used to find optimal bandwidth

Usage

1
2
3
RDOptBW.fit(d, M, kern = "triangular", opt.criterion, bw.equal = TRUE,
  alpha = 0.05, beta = 0.8, sclass = "H", order = 1,
  se.initial = "IKEHW")

Arguments

d

object of class "RDData"

M

Bound on second derivative of the conditional mean function.

kern

specifies kernel function used in the local regression. It can either be a string equal to "triangular" (k(u)=(1-|u|)_{+}), "epanechnikov" (k(u)=(3/4)(1-u^2)_{+}), or "uniform" (k(u)= (|u|<1)/2), or else a kernel function.

opt.criterion

Optimality criterion that bandwidth is designed to optimize. It can either be based on exact finite-sample maximum bias and finite-sample estimate of variance, or asymptotic approximations to the bias and variance. The options are:

"MSE"

Finite-sample maximum MSE

"FLCI"

Length of (fixed-length) two-sided confidence intervals.

"OCI"

Given quantile of excess length of one-sided confidence intervals

The finite-sample methods use conditional variance given by sigma2, if supplied. Otherwise, for the purpose of estimating the optimal bandwidth, conditional variance is assumed homoscedastic, and estimated using a nearest neighbor estimator.

bw.equal

logical specifying whether bandwidths on either side of cutoff should be constrainted to equal to each other.

alpha

determines confidence level, 1-alpha for constructing/optimizing confidence intervals.

beta

Determines quantile of excess length to optimize, if bandwidth optimizes given quantile of excess length of one-sided confidence intervals.

sclass

Smoothness class, either "T" for Taylor or "H" for Hölder class.

order

Order of local regression 1 for linear, 2 for quadratic.

se.initial

Method for estimating initial variance for computing optimal bandwidth. Ignored if data already contains estimate of variance.

"IKEHW"

Based on residuals from a local linear regression using a triangular kernel and IK bandwidth

"IKdemeaned"

Based on sum of squared deviations of outcome from estimate of intercept in local linear regression with triangular kernel and IK bandwidth

"Silverman"

Use residuals from local constant regression with uniform kernel and bandwidth selected using Silverman's rule of thumb, as in Equation (14) in IK

"SilvermanNN"

Use nearest neighbor estimates, rather than residuals

"NN"

Use nearest neighbor estimates, without assuming homoscedasticity

Value

a list with the following elements

hp

bandwidth for observations above cutoff

hm

bandwidth for observations below cutoff, equal to hp unless bw.equal==FALSE

sigma2m, sigma2p

estimate of conditional variance above and below cutoff, from d

Examples

1
2
3
## Lee data
d <- RDData(lee08, cutoff=0)
RDOptBW.fit(d, M=0.1, opt.criterion="MSE")[c("hp", "hm")]

kolesarm/RDHonest documentation built on April 3, 2018, 11:08 a.m.