fwildclusterboot

The fwildclusterboot package provides a native R implementation of the fast wild cluster bootstrap algorithm developed in Roodman et al (2019) for regression objects in R.

It also ports functionality of WildBootTests.jl to R via the JuliaConnectoR.

The package’s central function is boottest(). It allows to test univariate hypotheses using a wild cluster bootstrap at extreme speed: via the ‘fast’ algorithm, it is possible to run a wild cluster bootstrap with iterations in less than a second!

fwildclusterboot supports the following features:

• The wild bootstrap for OLS (Wu 1986).
• The wild cluster bootstrap for OLS (Cameron, Gelbach & Miller 2008, Roodman et al, 2019)
• The subcluster bootstrap (MacKinnon and Webb 2018).
• Confidence intervals formed by inverting the test and iteratively searching for bounds.
• Multiway clustering.
• One-way fixed effects.

Additional features are provided through WildBootTests.jl:

• The Wild Restricted Efficient bootstrap (WRE) for IV/2SLS/LIML (Davidson & MacKinnon, 2010).
• Arbitrary and multiple linear hypotheses in the parameters.

fwildclusterboot supports the following models:

• OLS: lm (from stats), fixest (from fixest), felm from (lfe)
• IV: ivreg (from ivreg).

The boottest() function

For a longer introduction to fwildclusterboot, take a look at the vignette.

library(fwildclusterboot)

# set seed via dqset.seed for boot_algo = "R" & Rademacher, Webb & Normal weights
dqrng::dqset.seed(2352342)
# set 'familiar' seed for all other algorithms and weight types 
set.seed(23325)

data(voters)

# fit the model via fixest::feols(), lfe::felm() or stats::lm()
lm_fit <- lm(proposition_vote ~ treatment  + log_income + as.factor(Q1_immigration) + as.factor(Q2_defense), data = voters)
# bootstrap inference via boottest()
lm_boot <- boottest(lm_fit, clustid = c("group_id1"), B = 9999, param = "treatment", seed = 1)
summary(lm_boot)
#> boottest.lm(object = lm_fit, param = "treatment", B = 9999, clustid = c("group_id1"), 
#>     seed = 1)
#>  
#>  Hypothesis: 1*treatment = 0
#>  Observations: 300
#>   Bootstr. Type: rademacher
#>  Clustering: 1-way
#>  Confidence Sets: 95%
#>  Number of Clusters: 40
#> 
#>              term estimate statistic p.value conf.low conf.high
#> 1 1*treatment = 0    0.079     3.983       0     0.04     0.118

Installation

You can install compiled versions offwildclusterboot from CRAN and the development version from R-universe (compiled) or github by following one of the steps below:

# from CRAN 
install.packages("fwildclusterboot")
# from r-universe (windows & mac, compiled R > 4.0 required)
install.packages('fwildclusterboot', repos ='https://s3alfisc.r-universe.dev')
# dev version from github
# note: installation requires Rtools
library(devtools)
install_github("s3alfisc/fwildclusterboot")

To run WildBootTests.jl through fwildclusterboot, Julia and WildBootTests.jl need to be installed.

You can install Julia by following the steps described on the official Julia homepage: https://julialang.org/downloads/. WildBootTests.jl can then be installed via Julia’s package management system.

To install WildBootTests.jl and Julia from within R, you can alternatively use the JuliaConnectoR.utils package.

devtools::install_github("s3alfisc/JuliaConnectoR.utils")
library(JuliaConnectoR.utils)
install_julia() # install Julia
connect_julia_r() # instructions to connect Julia and R
install_julia_packages("WildBootTests.jl") # install WildBootTests.jl
set_julia_ntreads() # instructions to set nthreads for Julia