setBoottest_engine

Sets the default bootstrap algo for the current R session
to be run via <code>boottest()</code> and <code>mboottest()</code>

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

setBoottest_engine function

<dl><dt>engine</dt>
<dd>Character scalar. Either 'R' or 'WildBootTests.jl'.
Default is 'R'</dd></dl>

Arguments

Sets the default bootstrap algo for the current R session
to be run via boottest() and mboottest() — setBoottest_engine

<dl>

<dt>engine</dt>
<dd>Character scalar. Either 'R' or 'WildBootTests.jl'.
Default is 'R'</dd>

</dl>

setBoottest_engine: Sets the default bootstrap algo for the current R session to be run via `boottest()` and `mboottest()`

Description

Usage

Value

Arguments

Examples