RDTEfficiencyBound: Finite-sample efficiency bounds for minimax CIs

Description Usage Arguments

View source: R/RD_opt.R


Compute efficiency of minimax one-sided CIs at constant functions and half-length of Pratt CIs.


RDTEfficiencyBound(d, M, opt.criterion = "FLCI", alpha = 0.05,
  beta = 0.5, se.initial = "IKEHW")



object of class "RDData"


Bound on second derivative of the conditional mean function.


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.


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.


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 Imbens and Kalyanaraman bandwidth


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


Use residuals from local constant regression with uniform kernel and bandwidth selected using Silverman's rule of thumb, as in Equation (14) in Imbens and Kalyanaraman (2012)


Use nearest neighbor estimates, rather than residuals


Use nearest neighbor estimates, without assuming homoscedasticity

kolesarm/RDHonest documentation built on Jan. 14, 2019, 7:04 a.m.