A tutorial on tidy cross-validation with R
Analyzing NetHack data, part 1: What kills the players
Analyzing NetHack data, part 2: What players kill the most
Building a shiny app to explore historical newspapers: a step-by-step guide
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2
Dealing with heteroskedasticity; regression with robust standard errors using R
Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
Forecasting my weight with R
From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
Getting data from pdfs using the pdftools package
Getting the data from the Luxembourguish elections out of Excel
Going from a human readable Excel file to a machine-readable csv with {tidyxl}
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Looking into 19th century ads from a Luxembourguish newspaper with R
Making sense of the METS and ALTO XML standards
Manipulate dates easily with {lubridate}
Manipulating strings with the {stringr} package
Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
Missing data imputation and instrumental variables regression: the tidy approach
Objects types and some useful R functions for beginners
Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`
R or Python? Why not both? Using Anaconda Python within R with {reticulate}
Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach
Some fun with {gganimate}
The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse
Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century
Using a genetic algorithm for the hyperparameter optimization of a SARIMA model
Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods
What hyper-parameters are, and what to do with them; an illustration with ridge regression
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
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.