Fast Wild Cluster Bootstrap Inference for Linear Regression
Models
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').
collapse, Formula, Rcpp, dreamerr, Matrix, Matrix.utils,
generics
fixest, lfe, data.table, fabricatr, tinytest, covr, knitr,
rmarkdown, spelling, broom, modelsummary

collapse, Formula, Rcpp, dreamerr, Matrix, Matrix.utils,
generics, gtools

fixest, lfe, plm, clusterSEs, data.table, fabricatr,
tinytest, covr, knitr, rmarkdown, spelling, broom, modelsummary

fixest, lfe, plm, clusterSEs, data.table, fabricatr,
tinytest, covr, knitr, rmarkdown, spelling, broom,
modelsummary, RStata

collapse, Formula, Rcpp, dreamerr, Matrix, Matrix.utils,
generics, gtools, dqrng
fixest, lfe, data.table, fabricatr, tinytest, covr, knitr,
rmarkdown, spelling, broom, modelsummary, RStata

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 - 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').
fixest, lfe, data.table, fabricatr, tinytest, covr, knitr,
rmarkdown, broom, modelsummary, RStata