# fwildclusterboot

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)).

### The `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 0.119
```

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

, please follow this link.

### Benchmarks

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).

### Installation

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")
```