evaluate_boot

An implementation of easy tools for outlier robust inference in
two-stage least squares (2SLS) models. The user specifies a reference
distribution against which observations are classified as outliers or not.
After removing the outliers, adjusted standard errors are automatically
provided. Furthermore, several statistical tests for the false outlier
detection rate can be calculated. The outlier removing algorithm can be
iterated a fixed number of times or until the procedure converges. The
algorithms and robust inference are described in more detail in Jiao (2019)
<https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Jonas Kurle

robust2sls

Outlier Robust Two-Stage Least Squares Inference and Testing

evaluate_boot function

<dl><dt>r2sls_boot</dt>
<dd>An object of <a href="/link/class?package=robust2sls&version=0.2.2" data-mini-rdoc="robust2sls::class">class</a> <code>"r2sls_boot"</code>, as returned
by case_resampling.</dd>
<dt>iterations</dt>
<dd>An integer or numeric vector with values &gt;= 0 specifying
which bootstrap results to evaluate.</dd></dl>

Arguments

Evaluate bootstrap results — evaluate_boot

<dl>

<dt>r2sls_boot</dt>
<dd>An object of <a href='https://rdrr.io/r/base/class.html'>class</a> <code>"r2sls_boot"</code>, as returned
by case_resampling.</dd>


<dt>iterations</dt>
<dd>An integer or numeric vector with values &gt;= 0 specifying
which bootstrap results to evaluate.</dd>

</dl>

evaluate_boot: Evaluate bootstrap results

Description

Usage

Value

Arguments