In s3alfisc/wildboottestjlr: Fast Wild Cluster Bootstrap Inference in R via WildBootTests.jl and JuliaConnectoR

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wildboottestjlr

wildboottestjlr ports the functionality of the WildBootTests.jl package to R via the JuliaConnectoR package. At the moment, it supports the following features of WildBootTests.jl:

• The wild bootstrap for OLS (Wu 1986).
• The Wild Restricted Efficient bootstrap (WRE) for IV/2SLS/LIML (Davidson and MacKinnon 2010).
• The subcluster bootstrap (MacKinnon and Webb 2018).
• Confidence intervals formed by inverting the test and iteratively searching for bounds.
• Multiway clustering.
• Arbitrary and multiple linear hypotheses in the parameters.
• One-way fixed effects.

The following model objects are currently supported:

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

In the future, IV methods for fixest and lfe will be added.

Installation / Getting Started

wildboottestjlr can be installed by running

library(devtools)
install_github("s3alfisc/wildboottestjlr")

You can install Julia by following the steps described here: 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 use wildboottestjlr::wildboottestjlr_setup(). Via wildboottestjlr_setup(), you can install Julia and WildBootTests.jl and connect R and Julia. You simply have to follow the instructions printed in the console!

library(wildboottestjlr)
wildboottestr_setup()

Similarly, you can set the number of Julia threads by running

julia_set_ntreads()

and following the instructions.

Example

wildboottestjlr's central function is boottest(). Beyond few minor differences, it largely mirrors the boottest() function from the fwildclusterboot package.

The Wild Bootstrap for OLS

library(wildboottestjlr)
# set a 'global' seed in the Julia session
set_julia_seed(rng = 12313452435)

data(voters)
library(fixest)
library(lfe)

# estimation via lm(), fixest::feols() or lfe::felm()
lm_fit <- lm(proposition_vote ~ treatment  + log_income, data = voters)
feols_fit <- feols(proposition_vote ~ treatment  + log_income, data = voters)
felm_fit <- felm(proposition_vote ~ treatment  + log_income, data = voters)

boot_lm <- boottest(lm_fit, clustid = "group_id1", B = 999, param = "treatment", rng = 7651427)
boot_feols <- boottest(feols_fit, clustid = "group_id1", B = 999, param = "treatment", rng = 7651427)
boot_felm <- boottest(felm_fit, clustid = "group_id1", B = 999, param = "treatment", rng = 7651427)

# summarize results via summary() method
#summary(boot_lm)

# also possible: use msummary() from modelsummary package
library(modelsummary)
msummary(list(boot_lm, boot_feols, boot_felm), 
        estimate = "{estimate} ({p.value})", 
        statistic = "[{conf.low}, {conf.high}]"
        )  

# plot(boot_lm)

The Wild Bootstrap for IV (WRE)

If boottest() is applied based on an object of type ivreg, the WRE bootstrap Davidson & MacKinnon (2010) is run.

library(ivreg)
data("SchoolingReturns", package = "ivreg")
data <- SchoolingReturns

ivreg_fit <- ivreg(log(wage) ~ education + age + ethnicity + smsa + south + parents14 |
                  nearcollege + age  + ethnicity + smsa + south + parents14,
                data = data)

boot_ivreg <- boottest(object = ivreg_fit, B = 999, param = "education", clustid = "fameducation", type = "webb")

summary(boot_ivreg)

Benchmarks

After compilation, wildboottestjlr is orders of magnitude faster than fwildclusterboot, in particular when the number of clusters N_G and the number of bootstrap iterations B get large.

library(ggplot2)
df <- data.table::fread("C:/Users/alexa/Dropbox/R package development/wildboottestjlr/benchmarks_df")
df$B <- factor(df$B, levels = c("10K", "100K"))
df$N_G <- factor(df$N_G, levels = c("N_G = 20", "N_G = 50", "N_G = 100", "N_G = 500", "N_G = 1000"))

ggplot(data = df, aes(x = B, y = time, color = type)) + 
  facet_wrap(~N_G, nrow = 1) + 
  geom_point() + 
scale_y_continuous(trans='log10') + 
  theme_bw() + 
  xlab("Bootstrap iterations") + 
  ylab("time in seconds, log scale")

The benchmarks plot the median value of 3 runs of a linear regression with N = 10.000 and k = 21.

s3alfisc/wildboottestjlr documentation built on June 16, 2022, 7:40 a.m.