About Me Blog
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

Update to Introduction to programming econometrics with R

This semester I taught a course on applied econometrics with the R programming language. For this, I created a document that I gave to my students and shared online. This is the kind of document I would have liked to read when I first started using R. I already had some programming experience in C and Pascal but this is not necessarily the case for everyone that is confronted to R when they start learning about econometrics.

This is why the beginning of the document focuses more on general programming knowledge and techniques, and then only on econometrics. People online seemed to like the document, as I’ve received some positive comments by David Smith from Revolution R (read his blog post about the document here) and Dave Giles which links to David’s blog post here. People on twitter have also retweeted David’s and Dave’s tweets to their blog posts and I’ve received some requests by people to send them the PDF by email (because they live in places where Dropbox is not accessible unfortunately).

The document is still a work in progress (and will probably remain so for a long time), but I’ve added some new sections about reproducible research and thought that this update could warrant a new blog post.

For now, only linear models are reviewed, but I think I’ll start adding some chapters about non-linear models soonish. The goal for these notes, however, is not to re-invent the wheel: there are lots of good books about econometrics with R out there and packages that estimate a very wide range of models. What I want for these notes, is to focus more on the programming knowledge an econometrician needs, in a very broad and general sense. I want my students to understand that R is a true programming language, and that they need to use every feature offered by such a language, and not think of R as a black box into which you only type pre-programmed commands, but also be able to program their own routines.

Also, I’ve made it possible to create the PDF using a Makefile. This may be useful for people that do not have access to Dropbox, but are familiar with git.

You can compile the book in two ways: first download the whole repository:

git clone git@bitbucket.org:b-rodrigues/programmingeconometrics.git

and then, with Rstudio, open the file appliedEconometrics.Rnw and knit it. Another solution is to use the Makefile. Just type:


inside a terminal (should work on GNU+Linux and OSX systems) and it should compile the document. You may get some message about “additional information” for some R packages. When these come up, just press Q on your keyboard to continue the compilation process.

Get the notes here.

As always, if you have questions or suggestions, do not hesitate to send me an email and I sure hope you’ll find these notes useful!