voters

datasets

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

voters function

<p>An object of class <code>data.frame</code> with 300 rows and 13 columns.</p>

Format

Random example data set — voters

Random example data set

voters: Random example data set

Description

Usage

Arguments

Format

Examples