The `fwildclusterboot`

package is an R port of STATA’s
boottest package.

It implements the fast wild cluster bootstrap algorithm developed in
Roodman et al
(2019) for
regression objects in R. It currently works for regression objects of
type `lm`

, `felm`

and `fixest`

from base R and the `lfe`

and `fixest`

packages.

The package’s central function is `boottest()`

. It allows the user to
test two-sided, univariate hypotheses using a wild cluster bootstrap.
Importantly, it uses the “fast” algorithm developed in Roodman et al,
which makes it feasible to calculate 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.

The `fwildclusterboot`

package currently supports multi-dimensional
clustering and one-dimensional, two-sided hypotheses. It supports
regression weights, multiple distributions of bootstrap weights, fixed
effects, restricted (WCR) and unrestricted (WCU) bootstrap inference and
subcluster bootstrapping for few treated clusters (MacKinnon & Webb,
(2018)).

`boottest()`

function```
library(fixest)
library(fwildclusterboot)
data(voters)
# fit the model via fixest::feols(), lfe::felm() or stats::lm()
feols_fit <- feols(proposition_vote ~ treatment + log_income | Q1_immigration + Q2_defense, data = voters)
# bootstrap inference via boottest()
feols_boot <- boottest(feols_fit, clustid = c("group_id1"), B = 9999, param = "treatment")
summary(feols_boot)
#> boottest.fixest(object = feols_fit, clustid = c("group_id1"),
#> param = "treatment", B = 9999)
#>
#> 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 treatment 0.079 4.123 0 0.039 0.118
```

For a longer introduction to the package’s key function, `boottest()`

,
please follow this
link.

Results of timing benchmarks of `boottest()`

, with a sample of N =
50000, k = 19 covariates and one cluster of dimension N_G (10
iterations each).

You can install `fwildclusterboot`

from CRAN or the development version
from github by following the steps below:

```
# from CRAN
install.packages("fwildclusterboot")
# dev version from github
# note: installation requires Rtools
library(devtools)
install_github("s3alfisc/fwildclusterboot")
```

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