Using R for Introductory Econometrics

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Welcome to the companion web site to the book

Using R for Introductory Econometrics, 2nd editionNew!
by Florian Heiss
ISBN: 979-8648424364

It can be

Comments on the first edition:

"A very nice resource for those wanting to use R in their introductory econometrics courses."
(Jeffrey M. Wooldridge)

"Using R for Introductory Econometrics is a fabulous modern resource. I know I'm going to be using it with my students, and I recommend it to anyone who wants to learn about econometrics and R at the same time."
(David E. Giles)

Content and Approach

This book introduces the popular, powerful and free programming language and software package R with a focus on the implementation of standard tools and methods used in econometrics. Unlike other books on similar topics, it does not attempt to provide a self-contained discussion of econometric models and methods. Instead, it builds on the excellent and popular textbook "Introductory Econometrics" by Jeffrey M. Wooldridge. Some other editions and versions work as well, see below. It is compatible in terms of topics, organization, terminology and notation, and is designed for a seamless transition from theory to practice. Topics include:

The chapters have the same names and cover the same material as the respective chapters in Wooldridge’s textbook. Assuming the reader is familiar with the concepts discussed there, this book explains and demonstrates how to implement everything in R and replicates many textbook examples. We also open some black boxes of the built-in functions for estimation and inference by directly applying the formulas known from the textbook to reproduce the results. Some supplementary analyses such as Monte Carlo simulations provide additional intuition and insights.

The book is designed mainly for students of introductory econometrics who ideally use Wooldridge’s “Introductory Econometrics” as their main textbook. It can also be useful for readers who are familiar with econometrics and possibly other software packages, such as Stata. For them, it offers an introduction to R and can be used to look up the implementation of standard econometric methods.

All computer code used in this book can be downloaded to make it easier to replicate the results and tinker with the specifications.

What's new in the second edition?

Many readers have contributed by pointing out errors and other problems, asking questions that helped to identify unclear explanations and making suggestions for improvements. I am especially grateful to Gawon Yoon, Liviu Andronic, Daniel Gerigk, Daniel Brunner and Lars Grönberg.

Also Interested in Python?

There is also a new sister book “Using Python for Introductory Econometrics”, coauthored by Daniel Brunner and published at the same time as this second edition of the R book. We are using the same structure, the same examples, and even much of the same text where it makes sense. This decision was not only made for laziness. It also helps readers to easily switch back and forth between the books. And if somebody worked through this R book, she can easily look up the pythonian way to achieve exactly the same results and vice versa, making it especially easy to learn both languages.

Which one should you start with (given your professor hasn’t made the decision for you)? Both share many of the advantages like having a huge and active user community, being widely used inside and outside of academia and being freely available. R is traditionally used in statistics, while Python is dominant in machine learning and artificial intelligence. These origins are still somewhat reflected in the availability of specialized extension packages. But most of all data analysis and econometrics tasks can be equally well performed in both packages. At the end, it’s most important point is to get used to the workflow of some dedicated data analysis software package instead of not using any software or a spreadsheet program for data analysis.

Note about other "Introductory Econometrics" versions

About the book

The book started as a collection of notes to myself on how to do stuff in R. I expanded and annotated it and to make it available to my students. After it looked more and more like a book, I decided to put more effort into it and actually make it available to the public. The book is self-published and not professionally edited. Once you get over the hideous layout and appalling grammar, you can start enjoying the benefits: