get_est_b1x

Computes the bias estimate for given parameters.

A novel bias-bound approach for non-parametric inference is introduced,
focusing on both density and conditional expectation estimation.
It constructs valid confidence intervals that account for the presence of
a non-negligible bias and thus make it possible to perform inference with
optimal mean squared error minimizing bandwidths.
This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

Xinyu DAI

rbbnp

A Bias Bound Approach to Non-Parametric Inference

Susanne M Schennach

get_est_b1x function

<dl><dt>X</dt>
<dd>A numerical vector representing the sample data of variable X.</dd>
<dt>h</dt>
<dd>A scalar bandwidth parameter.</dd>
<dt>est_Ar</dt>
<dd>A vector containing the estimated A and r parameters.</dd>
<dt>inf_k_ft</dt>
<dd>A kernel Fourier transform function.</dd>
<dt>...</dt>
<dd>Additional arguments passed to the quadgk integration function.</dd></dl>

Arguments

Estimation of bias b1x — get_est_b1x

<dl>

<dt>X</dt>
<dd>A numerical vector representing the sample data of variable X.</dd>


<dt>h</dt>
<dd>A scalar bandwidth parameter.</dd>


<dt>est_Ar</dt>
<dd>A vector containing the estimated A and r parameters.</dd>


<dt>inf_k_ft</dt>
<dd>A kernel Fourier transform function.</dd>


<dt>...</dt>
<dd>Additional arguments passed to the quadgk integration function.</dd>

</dl>

get_est_b1x: Estimation of bias b1x

Description

Usage

Value

Arguments