Fast Wild Cluster Bootstrap Inference for Linear Models
Simple tool that aggregates the value of CATT coefficients in staggere...
set the small sample correction factor applied in boottest()
Fast wild cluster bootstrap inference for object of class felm
Fast wild cluster bootstrap inference for object of class fixest
Fast wild cluster bootstrap inference for object of class lm
Fast wild cluster bootstrap inference for object of class lm
Fast wild cluster bootstrap inference
S3 method to obtain wild cluster bootstrapped confidence intervals
Check if julia or python are installed / can be found on the PATH.
S3 method to glance at objects of class boottest
S3 method to glance at objects of class boottest
Fast wild cluster bootstrap inference for joint hypotheses for object ...
Fast wild cluster bootstrap inference for joint hypotheses for object ...
Fast wild cluster bootstrap inference of joint hypotheses for object o...
Arbitrary Linear Hypothesis Testing for Regression Models via Wald-Tes...
S3 method to obtain the effective number of observation used in `boott...
S3 method to obtain the effective number of observation used in `mboot...
Plot the bootstrap distribution of t-statistics
S3 method to print key information for objects of type bboottest
S3 method to print key information for objects of type mboottest
S3 method to obtain the wild cluster bootstrapped p-value of an object...
S3 method to obtain the wild cluster bootstrapped p-value of an object...
pval is a S3 method to collect pvalues for objects of type `boottest...
Objects exported from other packages
Sets the default bootstrap algo for the current R session to be run vi...
S3 method to summarize objects of class boottest
S3 method to summarize objects of class mboottest
S3 method to obtain the non-bootstrapped t-statistic calculated via `b...
S3 method to obtain the non-bootstrapped test statistic calculated via...
teststat is a S3 method to collect teststats for objects of type `bo...
S3 method to summarize objects of class boottest into tidy data.frame
S3 method to summarize objects of class mboottest into tidy data.frame
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.
Useful links