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
Curly-Curly, the successor of Bang-Bang
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
Fast food, causality and R packages, part 1
Fast food, causality and R packages, part 2
For posterity: install {xml2} on GNU/Linux distros
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
Get text from pdfs or images using OCR: a tutorial with {tesseract} and {magick}
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}
Historical newspaper scraping with {tesseract} and R
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Intermittent demand, Croston and Die Hard
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
Modern R with the tidyverse is available on Leanpub
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}
Split-apply-combine for Maximum Likelihood Estimation of a linear model
Statistical matching, or when one single data source is not enough
The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
The never-ending editor war (?)
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 cosine similarity to find matching documents: a tutorial using Seneca's letters to his friend Lucilius
Using linear models with binary dependent variables, a simulation study
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
{disk.frame} is epic
{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

*Abstract*

Reliable and reproducible research is an important cornerstone of science, and version control systems not only make reproducible research possible in a rapid and easy way, but also provide a way of collaborating with co-authors. The purpose of this methodological paper is to present Git, a very successful version control system and how it can be used by economists working together on their papers and the accompanying computer code. Version control systems also make sharing the findings with the rest of the scientific community more easy and streamlined. To understand how version control systems came to be, one must be familiar with the history of free software. In the introduction, I will present free software and its philosophy and show how version control systems make free software possible. In the second section I present Git which is a widely used version control system. In the third section I show a basic usage of Git. In the fourth section, I conclude.

*Abstract*

Using French administrative data, we estimate the impact of the birth of a first child on hourly and daily wages, as well as for hours worked, for both women and men. We compute the impact on these outcome variables, two, four and eight years after the birth of the child, using difference-in-differences. We compare the impact for different education levels of both mothers and fathers. The results show that in the short term there is no impact of a birth on wages and hours worked but eight years after the birth of a child, highly educated women suffer from a loss in daily and hourly wages as well as hours worked. Educated men also decrease their labour supply.

Vincent Vergnat

*Abstract*

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*Summary of the project*

This project aims to estimate a structural model of fertility and career decisions of university educated women for Germany and France.

You can download my Master’s Thesis here.

Here’s a short description:

The Great Moderation was a phenomenon characterized by great economic stability in all OECD countries. In my Master’s thesis, I study this phenomenon in the Euro Area using a medium-scale, Markov-switching DSGE model and find that the Great Moderation in the Euro Area was due to better policy by the monetary authorities.

I’m currently working on two papers in collaboration with people from the ZEW (Raphael Abiry, François Laisney, Holger Stichnoth), University of Bonn (Hans-Martin von Gaudecker) and the IFS (Richard Blundell) on fertility and labor supply of highly educated women in Germany and France (the first project is financed by the SEEK and the other by the ANR-DFG). We estimate a dynamic discrete choice model with human capital accumulation and skills transferability between jobs. Links to the papers will be posted when they’re done.

With my thesis advisor, Betrand Kœbel, we are currently working on a paper where we explain how an employer, (which can decide to delegate the wage fixing decision to an employee), and his employee can cooperate and attain higher pay-offs despite having conflicting objectives. For this, we estimate a dynamic model with GMM using data from Charness (2012).