boottest

<code>boottest</code> is a S3 method that allows for fast wild cluster
bootstrap inference for objects of class lm, fixest and felm by implementing
the fast wild bootstrap algorithm developed in Roodman et al., 2019.

Implementation of fast algorithms for wild cluster bootstrap
inference developed in 'Roodman et al' (2019, 'STATA' Journal,
<doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022),
which makes it feasible to quickly calculate bootstrap test
statistics based on a large number of bootstrap draws even for
large samples. Multiple bootstrap types as described in 'MacKinnon,
Nielsen & Webb' (2022) are supported.
Further, 'multiway' clustering, regression weights,
bootstrap weights, fixed effects and 'subcluster' bootstrapping
are supported. Further, both restricted ('WCR') and unrestricted
('WCU') bootstrap are supported. Methods are provided for a variety
of fitted models, including 'lm()', 'feols()'
(from package 'fixest') and 'felm()' (from package 'lfe').
Additionally implements a 'heteroskedasticity-robust' ('HC1') wild
bootstrap.
Last, the package provides an R binding to 'WildBootTests.jl',
which provides additional speed gains and functionality,
including the 'WRE' bootstrap for instrumental variable models
(based on models of type 'ivreg()' from package 'ivreg')
and hypotheses with q > 1.

Alexander Fischer

fwildclusterboot

Fast Wild Cluster Bootstrap Inference for Linear Models

David Roodman

Achim Zeileis

Nathaniel Graham

Susanne Koell

Laurent Berge

Sebastian Krantz

boottest function

<dl><dt>object</dt>
<dd>An object of type lm, fixest, felm or ivreg</dd>
<dt>...</dt>
<dd>other arguments</dd></dl>

Arguments

To guarantee reproducibility, you can either use <code>boottest()'s</code> <code>seed</code>
function argument, or
set a global random seed via<ul>
<li><code><a href="/link/set.seed()?package=fwildclusterboot&version=0.13.0" data-mini-rdoc="fwildclusterboot::set.seed()">set.seed()</a></code> when using<ol>
<li>the lean algorithm (via <code>engine = "R-lean"</code>), 2) the heteroskedastic
wild bootstrap</li>
<li>the wild cluster bootstrap via <code>engine = "R"</code> with Mammen weights
or 4) <code>engine = "WildBootTests.jl"</code></li>
</ol></li>
<li><code>dqrng::dqset.seed()</code> when using <code>engine = "R"</code> for Rademacher, Webb
or Normal weights</li>
</ul>

Setting Seeds

The fast wild cluster bootstrap algorithms are further implemented in the
following software packages:<ul>
<li>Stata:<a href="https://github.com/droodman/boottest">boottest</a></li>
<li>Julia:<a href="https://github.com/droodman/WildBootTests.jl">WildBootTests.jl</a></li>
<li>Python:<a href="https://github.com/s3alfisc/wildboottest">wildboottest</a></li>
</ul>

Stata, Julia and Python Implementations

Fast wild cluster bootstrap inference — boottest

<dl>

<dt>object</dt>
<dd>An object of type lm, fixest, felm or ivreg</dd>


<dt>...</dt>
<dd>other arguments</dd>

</dl>

To guarantee reproducibility, you can either use <code>boottest()'s</code> <code>seed</code>
function argument, or
set a global random seed via<ul>
<li><code><a href='https://rdrr.io/r/base/Random.html'>set.seed()</a></code> when using<ol>
<li>the lean algorithm (via <code>engine = "R-lean"</code>), 2) the heteroskedastic
wild bootstrap</li>
<li>the wild cluster bootstrap via <code>engine = "R"</code> with Mammen weights
or 4) <code>engine = "WildBootTests.jl"</code></li>
</ol></li>
<li><code>dqrng::dqset.seed()</code> when using <code>engine = "R"</code> for Rademacher, Webb
or Normal weights</li>
</ul>

The fast wild cluster bootstrap algorithms are further implemented in the
following software packages:<ul>
<li>Stata:<a href='https://github.com/droodman/boottest'>boottest</a></li>
<li>Julia:<a href='https://github.com/droodman/WildBootTests.jl'>WildBootTests.jl</a></li>
<li>Python:<a href='https://github.com/s3alfisc/wildboottest'>wildboottest</a></li>
</ul>

boottest: Fast wild cluster bootstrap inference

Description

Usage

Value

Arguments

Setting Seeds

Stata, Julia and Python Implementations

References

See Also

Examples