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README.Rmd
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README.Rmd
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---
output: github_document
always_allow_html: true
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
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# wildboottestjlr
`wildboottestjlr` ports the functionality of the [WildBootTests.jl](https://github.com/droodman/WildBootTests.jl) package to R via the [JuliaConnectoR](https://github.com/stefan-m-lenz/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
```{r, eval = FALSE}
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!
```{r, eval = FALSE}
library(wildboottestjlr)
wildboottestr_setup()
```
Similarly, you can set the number of Julia threads by running
```{r, eval = FALSE}
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](https://github.com/s3alfisc/fwildclusterboot) package.
### The Wild Bootstrap for OLS
```{r, message = FALSE, warning = FALSE}
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)](https://www.tandfonline.com/doi/abs/10.1198/jbes.2009.07221) is run.
```{r, message = FALSE, warning = FALSE}
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.
```{r, fig.width=10, fig.height=3, echo = FALSE, warning = FALSE, message = FALSE}
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.