get_bootstrap_pvalue

<p>Character vector of length 1. Type of p-value.
Options include "two-tailed", "equal-tailed", "&gt;" and "&lt;".</p>

p_val_type

t_stat

t_boot

<p>get bootstrapped p-value based on bootstrapped t-stats</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

get_bootstrap_pvalue function

get bootstrapped p-value based on bootstrapped t-stats — get_bootstrap_pvalue

get bootstrapped p-value based on bootstrapped t-stats

get_bootstrap_pvalue: get bootstrapped p-value based on bootstrapped t-stats

Description

Usage

Arguments

Value