1 Preface
This Rbook introduces students to econometrics 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 econometrics students the best tools for their labor market and adhere to modern statistics teaching guidelines. The Rbook is organized into three parts
- Part I: Data Analysis in R
- Part II: Linear Regression in R
- Part III: Reproducible Research in R
Part I introduces students to the basics of programming and statistical analysis of economic data using R. There are many practical examples, including on how to analyze data interactively and communicate results. I aimed to replace mathematics with simulations whenever possible. We also cover statistical reporting using R + markdown, which research suggests is a good combination 1 2.
Part II refines material from several introductory econometrics textbooks and covers linear models only from a “minimum distance” perspective. (We operate under the maxim “All models are wrong” and do not prove unbiasedness.) Also included is a novel chapter on “Data scientism” that more clearly illustrates the ways that simplistic approaches can mislead rather than illuminate. (I stress “gun safety” instead of “pull to shoot”, which I feel is missing from many textbooks.) Overall, there is a more humble view towards what we can infer from linear regressions and more room for economic theory in model development and interpretation.
Part III synthesize a lot of programming guidance and examples now available on the internet. This is useful for semester projects and beyond.
Although any interested reader may find it useful, this Rbook is primarily developed for my students. It could serve as a technical prerequisite for more advanced courses (such as nonparametric statistics or structural econometrics.)
Please report any errors or issues at https://github.com/Jadamso/Rbooks/issues.
Last updated: 16.04.2025