1 Preface

This Rbook contains three primers that should get you started with econometrics in R

• Part I: Programming in R
• Part II: Linear Regression in R
• Part III: Reproducible Research in R

Parts I and III synthesize a lot of programming guidance and examples now available on the internet. Much of Part II originally came from other introductory econometrics textbooks. In contrast, however, this Rbook focuses on understanding statistics without parametric assumptions and formulas. In many ways, it is a modern version of “Introductory Econometrics: Using Monte Carlo Simulation with Microsoft Excel” by Barreto and Howland, updated to give students the best tools for their labor market and adhere to modern statistics teaching guidelines. Along these lines, many practical programming examples are also included, such as how to analyze data interactively and communicate results.

Notably, this Rbook covers linear models only from a “minimum distance” perspective. We operate under the maxim “All models are wrong” and do not prove unbiasedness. This serves as a technical prerequisite for more advanced courses on either nonparametric statistics or structural economic models. There is also a novel chapter on “Data scientism” that more clearly illustrates the ways that simplistic approaches can mislead rather than illuminate (perhaps analogous to a gun safety course). Overall, their is a more humble view towards what we can infer from statistics alone and more room for economic theory in model development and interpretation.

Although any interested reader may find it useful, it is being primarily developed for my students. Please report any errors or issues at https://github.com/Jadamso/Rbooks/issues.

Last updated: 19.05.2024