setBoottest_boot_algo

<p>Character scalar. Either 'R' or 'WildBootTests.jl'. Default is 'R'</p>

boot_algo

<p>Sets the bootstrap algo to be run via <code>boottest()</code> and <code>waldboottest()</code></p>

Implementation of the fast algorithm for wild cluster bootstrap
inference developed in Roodman et al (2019, STATA Journal) for
linear regression models <doi:10.1177/1536867X19830877>,
which makes it feasible to quickly calculate bootstrap test
statistics based on a large number of bootstrap draws even for
large samples. 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.
Further, 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_boot_algo function

Sets the bootstrap algo to be run via <code>boottest()</code> and <code>waldboottest()</code> — setBoottest_boot_algo

Sets the bootstrap algo to be run via <code>boottest()</code> and <code>waldboottest()</code>

setBoottest_boot_algo: Sets the bootstrap algo to be run via `boottest()` and `waldboottest()`

Description

Usage

Arguments

Value