This website is meant to be a resource for Econometrics students learning R. It is both an R package and a series of articles that explain how to use R for statistics and how to do it in a zen-like way. It is intended as a complement to two other books: Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer; and R for Data Science by Hadley Wickham and Garrett Grolemund.
Introduction to Econometrics with R follows Jeffery Wooldridge’s textbook of the same name and shows how to implement selected applications. Most intro econometrics classes will cover only portions of Wooldridge and sometimes stray quite far from the material. Hence, this website tries to be a concise complement to Introduction to Econometrics with R, offering up practical tools for the different things I have seen covered in metrics courses.
R for Data Science is a broad overview of R and the tidyverse (a collection of packages within R). It starts slow at a beginner level and eventually covers a vast array of important concepts. It’s highly recommended (perhaps required) reading for new R programmers. This website partly assumes the background information contained in R for Data Science and builds on it with specific recommendations for econometrics and academic research.
Purpose of the Package
1. Metapackage
A metapackage is a package that contains other packages. When you install this package, you will also install a curated list of packages that I have created. I feel this is the fastest way to get you “set up” with a complete environment where you can do your econometrics in R.
2. Documentation
The series of articles on this website are created from TA review sessions where I covered important aspects of programming in R. There are two sections:
- The Art of Metrics contains articles on the basic estimation and inference techniques from an advanced undergraduate or first-semester graduate econometrics class. It focuses heavily on comparisons with Stata and aims to build a foundational set of metrics tools.
- The Zen of Programming has a completely different focus from the previous section. What budding computer scientists (a.k.a. you) sometimes miss is that there is a whole world of non-technical skills that make programming more productive, fun, and reproducible. This has been dubbed the “Hidden Curriculum” of data science by Scott Cunningham. The Zen of Programming aims to take the “hidden” out of that title and teach a set of skills for project management that help you produce high-quality, shareable, and understandable academic research.
3. Code
There are a few things that are complicated to program, and in those cases I try to write my own functions for you to use. You’re free to use them in your econometrics homeworks, with the understanding that
- You assume all liability if the function gives incorrect answers. I thoroughly test and validate the functions that I write, but I don’t have the desire or time to spend tinkering with them incessantly.
- You will eventually have to figure it out yourself. Your employer won’t like you loading some grad student’s GitHub repo into a million-dollar software project, so you will eventually have to know how to implement the function.
If that sounds dandy to you, head on over to the Get Started page for installation instructions, and then start reading some articles!