LPPHonest.fit: Honest inference at a point

Description Usage Arguments Value

View source: R/LPP_lp.R

Description

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

Usage

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LPPHonest.fit(d, M, kern = "triangular", h, opt.criterion, alpha = 0.05,
  beta = 0.8, se.method = "nn", J = 3, sclass = "H", order = 1,
  se.initial = "ROTEHW")

Arguments

d

object of class "LPPData"

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.

h

Bandwidth. If not supplied, optimal bandwidth is computed according to criterion given by opt.criterion.

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.

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.

se.method

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:

"nn"

Nearest neighbor method

"EHW"

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

"demeaned"

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.

"plugin"

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

"supplied.var"

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

J

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

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.

"ROTEHW"

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

"ROTdemeaned"

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

Value

Returns an object of class "LPPResults", see description in LPPHonest


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