Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Introducing brotools
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

I’m currently working on a paper (with my colleague Vincent Vergnat who is also a Phd candidate at BETA) where I want to estimate the causal impact of the birth of a child on hourly and daily wages as well as yearly worked hours. For this we are using non-parametric difference-in-differences (henceforth DiD) and thus have to bootstrap the standard errors. In this post, I show how this is possible using the function `boot`

.

For this we are going to replicate the example from Wooldridge’s *Econometric Analysis of Cross Section and Panel Data* and more specifically the example on page 415. You can download the data for R here. The question we are going to try to answer is *how much does the price of housing decrease due to the presence of an incinerator in the neighborhood?*

First put the data in a folder and set the correct working directory and load the `boot`

library.

```
library(boot)
setwd("/home/path/to/data/kiel data/")
load("kielmc.RData")
```

Now you need to write a function that takes the data as an argument, as well as an indices argument. This argument is used by the `boot`

function to select samples. This function should return the statistic you’re interested in, in our case, the DiD estimate.

```
run_DiD <- function(my_data, indices){
d <- my_data[indices,]
return(
mean(d$rprice[d$year==1981 & d$nearinc==1]) -
mean(d$rprice[d$year==1981 & d$nearinc==0]) -
(mean(d$rprice[d$year==1978 & d$nearinc==1]) -
mean(d$rprice[d$year==1978 & d$nearinc==0]))
)
}
```

You’re almost done! To bootstrap your DiD estimate you just need to use the boot function. If you have cpu with multiple cores (which you should, single core machines are quite outdated by now) you can even parallelize the bootstrapping.

`boot_est <- boot(data, run_DiD, R=1000, parallel="multicore", ncpus = 2)`

Now you should just take a look at your estimates:

`boot_est`

`ORDINARY NONPARAMETRIC BOOTSTRAP`

Call: boot(data = data, statistic = run_DiD, R = 1000, parallel = "multicore", ncpus = 2)

`Bootstrap Statistics : original bias std. error t1* -11863.9 -553.3393 8580.435`

These results are very similar to the ones in the book, only the standard error is higher.

You can get confidence intervals like this:

`quantile(boot_est$t, c(0.025, 0.975))`

```
## 2.5% 97.5%
## -30186.397 3456.133
```

or a t-statistic:

`boot_est$t0/sd(boot_est$t)`

`## [1] -1.382669`

Or the density of the replications:

`plot(density(boot_est$t))`

Just as in the book, we find that the DiD estimate is not significant to the 5% level.