extract_boot

Extracts bootstrap results for a specific iteration

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

extract_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>iteration</dt>
<dd>An integer &gt;= 0 specifying which bootstrap results to
extract.</dd></dl>

Arguments

Extracts bootstrap results for a specific iteration — extract_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>iteration</dt>
<dd>An integer &gt;= 0 specifying which bootstrap results to
extract.</dd>

</dl>

extract_boot: Extracts bootstrap results for a specific iteration

Description

Usage

Value

Arguments