Applied Econometrics with R (Use R!)

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

• Using R for Introductory Econometrics
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Mastering 'Metrics: The Path from Cause to Effect
• Mostly Harmless Econometrics: An Empiricist's Companion
• Introductory Time Series with R (Use R!)
• Introduction to R for Quantitative Finance
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Microeconometrics: Methods and Applications
• R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)