voters

datasets

Implementation of the fast algorithm for wild cluster bootstrap
inference developed in Roodman et al (2019, STATA Journal) for
linear regression models
<https://journals.sagepub.com/doi/full/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 - as long as the number of bootstrapping clusters
is not too large. 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').

Alexander Fischer

fwildclusterboot

Fast Wild Cluster Bootstrap Inference for Linear Regression
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