# Bootstrap Standard Errors

Boostrapping is a statistical method that uses random sampling with replacement to determine the sampling variation of an estimate. If you have a data set of size \(N\), then (in its simplest form) a “bootstrap sample” is a data set that randomly selects \(N\) rows from the original data, perhaps taking the same row multiple times. In fact, each observation has the same probability of being selected for each bootstrap sample. For more information, see Wikipedia.

Bootstrap is commonly used to calculate standard errors. If you produce many bootstrap samples and calculate a statistic in each of them, then under certain conditions, the distribution of that statistic across the bootstrap samples is the sampling distribution of that statistic. So the standard deviation of the statistic across bootstrap samples can be used as an estimate of standard error. This approach is generally used in cases where calculating the analytical standard error of a statistic would be too difficult or impossible.

## Keep in Mind

- Although it feels entirely data-driven, bootstrap standard errors rely on assumptions just like everything else. It assumes your original model is correctly specified, for example. Basic bootstrapping assumes observations are independent of each other.
- It is possible to allow for correlations across units by using block-bootstrap.
- Bootstrapping can also be used to calculate other features of the parameter’s sample distribution, like the percentile, not just the standard error.

## Also Consider

- This page will consider the simplest approach to bootstrapping (the basic resampling of rows), but there are many others, such as cluster (or blocked) bootstrap, Bayesian bootstrap, and Wild bootstrap. For more information, see Wikipedia. Check the help files of the bootstrap package you’re using to see if they support these approaches.
- Bootstrap is relatively straightforward to program yourself: resample, calculate, repeat, and then look at the distribution. If your reason for doing bootstrap is because you want your standard errors to reflect an unusual sampling or data manipulation procedure, for example, you may be best off programming your own routine.
- This page contains a general approach to bootstrap, but for some statistical procedures, bootstrap standard errors are common enough that the command itself has an option to produce bootstrap standard errors. If this option is available, it is likely superior.

# Implementations

## R

The **sandwich** package (link] provides a convenient `vcovBS`

function for obtaining bootstrapped covariance-variance matrices, and thus standard errors, for a wide range of model classes in R. We normally combine this with the `coeftest`

function from the **lmtest** package, which allows us to substitute in the adjusted (here: bootstrapped) errors into our model, post-estimation.

```
# If necessary
# install.packages('sandwich','lmtest')
library(sandwich)
library(lmtest)
# Use in-built mtcars data
data(mtcars)
# Run a regression with normal (iid) errors
m <- lm(hp~mpg + cyl, data = mtcars)
# Obtain the boostrapped SEs
coeftest(m, vcov = vcovBS(m))
```

Another approach to obtaining bootstrapping standard errors in R is to use the **boot** package (link). This is typcally more hands-on, but gives the user a lot of control over how the bootrapping procedure will execute.

```
# If necessary
# install.packages('boot','broom','stargazer')
# Load boot library
library(boot)
# Create function that takes
# A dataset and indices as input, and then
# performs analysis and returns a parameter of interest
regboot <- function(data, indices) {
m1 <- lm(hp~mpg + cyl, data = data[indices,])
return(coefficients(m1))
}
# Call boot() function using the function we just made with 200 bootstrap samples
# Note the option for stratified resampling with "strata", in addition to other options
# in help(boot)
boot_results <- boot(mtcars, regboot, R = 200)
# See results
boot_results
plot(boot_results)
# There are lots of diagnostics you can look at at this point,
# see https://statweb.stanford.edu/~tibs/sta305files/FoxOnBootingRegInR.pdf
# Optional: print regression table with the bootstrap SEs
# This uses stargazer, but the method is similar
# with other table-making packages,
# see /Presentation/export_a_formatted_regression_table.html
library(broom)
tidy_results <- tidy(boot_results)
library(stargazer)
m1 <- lm(hp~mpg + cyl, data = mtcars)
stargazer(m1, se = list(tidy_results$std.error), type = 'text')
```

## Stata

Many commands in Stata come with a `vce(bootstrap)`

option, which will implement bootstrap standard errors.

```
* Load auto data
sysuse auto.dta, clear
* Run a regression with bootstrap SEs
reg mpg weight length, vce(bootstrap)
* see help bootstrap to adjust options like number of samples
* or strata
reg mpg weight length, vce(bootstrap, reps(200))
```

Alternatively, most commands will also accept using the `bootstrap`

prefix. Even if they do not allow the option `vce(bootstrap)`

.

```
* If a command does not support vce(bootstrap), there's a good chance it will
* work with a bootstrap: prefix, which works similarly
bootstrap, reps(200): reg mpg weight length
```

If your model uses weights, `bootstrap`

prefix (or `vce(bootstrap)`

) will not be appropriate, and the above command may give you an error:

```
*This should give you an error
bootstrap, reps(200): reg mpg foreign length [pw=weight]
```

`bootstrap`

, however, can be used to estimate standard errors of more complex systems. This, however, require some programming. Below an example for bootstrapping marginal effects for `ivprobit`

.

```
webuse laborsup, clear
** Start creating a small program
program two_ivp, eclass
* estimate first stage
reg other_inc male_educ fem_educ kids
* estimate residuals
capture drop res
predict res, res
* add them to the probit first stage
* This is what ivprobit two step does.
probit fem_work fem_educ kids other_inc res
margins, dydx(fem_educ kids other_inc) post
end
** now simply bootstrap the program:
bootstrap, reps(100):two_ivp
```