get_ssc

boot_ssc_object

The number of estimated parameters

A vector that helps create the covariance matrix

vcov_sign

Heteroskedastic wild bootstrap? FALSE by default. If
TRUE, cluster adjustments via G and vcov_sign will be ignored

heteroskedastic

Compute small sample adjustment factors

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

get_ssc: Compute small sample adjustment factors

Description

Usage

Arguments

Value