RDHonest.fit: Honest inference in RD

Description Usage Arguments Value

View source: R/RD_lp.R


Basic computing engine called by RDHonest to compute honest confidence intervals for local polynomial estimators.


RDHonest.fit(d, M, kern = "triangular", hp, hm = hp, opt.criterion,
  bw.equal = TRUE, alpha = 0.05, beta = 0.8, se.method = "nn", J = 3,
  sclass = "H", order = 1, se.initial = "IKEHW")



object of class "RDData"


Bound on second derivative of the conditional mean function.


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.

hp, hm

bandwidth for treated (units with positive running variable), and control (units with negative running variable) units. If hm is not supplied, it is assumed to equal to hp. If neither bandwidth is supplied, optimal bandwidth is computed according to criterion given by 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:


Finite-sample maximum MSE


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


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.


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


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


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


Vector with methods for estimating standard error of estimate. If NULL, standard errors are not computed. The elements of the vector can consist of the following methods:


Nearest neighbor method


Eicker-Huber-White, with residuals from local regression (local polynomial estimators only).


Use EHW, but instead of using residuals, estimate sigma^2_i by subtracting the estimated intercept from the outcome (and not subtracting the estimated slope). Local polynomial estimators only.


Plug-in estimate based on asymptotic variance. Local polynomial estimators in RD only.


Use conditional variance supplied by sigma2 / d instead of computing residuals


Number of nearest neighbors, if "nn" is specified in se.method.


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


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


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


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


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


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


Use nearest neighbor estimates, rather than residuals


Use nearest neighbor estimates, without assuming homoscedasticity


Returns an object of class "RDResults", see description in RDHonest

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