fwildclusterboot v0.8

Two new bootstrap algorithms: 'WildBootTests.jl' and 'R-lean'

boot_algo = 'WildBootTests.jl'

• fwildclusterboot now supports calling WildBootTests.jl, which is a very fast Julia implementation of the wild cluster bootstrap algorithm. To do so, a new function argument is introduced, boot_algo, through which it is possible to control the executed bootstrap algorithm.

# load data set voters included in fwildclusterboot
data(voters)
# estimate the regression model via lm
lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration , data = voters)
boot_lm <- boottest(
  lm_fit, 
  clustid = "group_id1", 
  param = "treatment", 
  B = 9999, 
  boot_algo = "WildBootTests.jl"
)

• WildBootTests.jl is (after compilation) orders of magnitudes faster than fwildclusterboot's native R implementation, and speed gains are particularly pronounced for large problems with a large number of clusters and many bootstrap iterations.

• Furthermore, WildBootTests.jl supports a range of models and tests that were previously not supported by fwildclusterboot: most importantly a) wild cluster bootstrap tests of multiple joint hypotheses and b) the WRE bootstrap by Davidson & MacKinnon for instrumental variables estimation. On top of the cake ... the WRE is really fast.

library(ivreg)
data("SchoolingReturns", package = "ivreg")
# drop all NA values from SchoolingReturns
SchoolingReturns <- SchoolingReturns[rowMeans(sapply(SchoolingReturns, is.na)) == 0,]
ivreg_fit <- ivreg(log(wage) ~ education + age + ethnicity + smsa + south + parents14 |
                           nearcollege + age  + ethnicity + smsa + south + parents14, data = SchoolingReturns)

boot_ivreg <- boottest(
  object = ivreg_fit,
  B = 999,
  param = "education",
  clustid = "kww",
  type = "mammen",
  impose_null = TRUE
)
generics::tidy(boot_ivreg)
#              term  estimate statistic   p.value    conf.low conf.high
# 1 1*education = 0 0.0638822  1.043969 0.2482482 -0.03152655 0.2128746

• For guidance on how to install and run WildBooTests.jl, have a look at the associated article.

• Also, note that running the wild cluster bootstrap through WildBootTests.jl is often very memory-efficient.

boot_algo = 'R-lean'

A key limitation of the vectorized 'fast' cluster bootstrap algorithm as implemented in fwildclusterboot is that it is very memory-demanding. For 'larger' problems, running boottest() might lead to out-of-memory errors. To offer an alternative, boottest() now ships a 'new' rcpp- and loop-based implementation of the wild cluster bootstrap (the 'wild2' algorithm in Roodman et al).

boot_lm <- boottest(
  lm_fit, 
  clustid = "group_id1", 
  param = "treatment", 
  B = 9999, 
  boot_algo = "R-lean"
)

Heteroskeadstic Wild Bootstrap

It is now possible to run boottest() without specifying a clustid function argument. In this case, boottest() runs a heteroskedasticity-robust wild bootstrap (HC1), which is implemented in c++.

boot_hc1 <- boottest(lm_fit, param = "treatment", B = 9999)
summary(boot_hc1)

boottest() function argument beta0 deprecated

For consistency with WildBootTests.jl, the boottest() function argument beta0 is now replaced by a new function argument, r.

Frühjahrsputz

I have spent some time to clean up fwildclusterboot's internals, which should now hopefully be more readable and easier to maintain.

Testing

fwildclusterboot is now pre-dominantly tested against WildBootTests.jl. Tests that depend on Julia are by default not run on CRAN, but are regularly run on Mac, Windows and Linux via github actions.