TableSnoopingCVNearBd: Table of snooping-adjusted critical values for estimation...

In kolesarm/BWSnooping: Confidence invervals robust to bandwidth snooping

Description

Generate a table of two-sided snooping-adjusted critical values for a given kernel in a local polynomial regression near a boudnary point

Usage

TableSnoopingCVNearBd(bwratios, kernel = "triangular", db, order = 1,
  alpha = 0.05, S = 10000, T = 1000, ngr = 100)

Arguments

bwratios	Bandwidth ratios of maximum to minimum bandwidth for which to compute critical values
kernel	Either one of "uniform", "triangular", or "epanechnikov", or else an (original) kernel function. Function computes appropriate equivalent kernel function
db	Local distance to boundary, equal to x_{0}/\underline{h}, where x_{0} is point of interest.
order	order of local polynomial
alpha	Determines confidence level 1-α at which to compute critical values
S	number of draws of the Gaussian process \hat{\mathbb{H}}(h)
T	number of draws from a normal distribution in each draw of the Gaussian process
ngr	number of grid points on which to evaluate the Gaussian process \hat{\mathbb{H}}(s), or else a vector of grid points

Value

A table of snooping-adjusted critical values

kolesarm/BWSnooping documentation built on May 20, 2019, 12:54 p.m.