# LPPHonest.fit: Honest inference at a point In kolesarm/RDHonest: Honest inference in sharp regression discontinuity designs

## Description

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

## Usage

 ```1 2 3``` ```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.