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. Along these lines, many practical programming examples are also included, including on how to analyze data interactively and communicate results. This book also serves as a technical prerequisite for more advanced courses (such as nonparametric statistics or structural econometrics.)
The Rbook is organized into three parts
- 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. 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.
Although any interested reader may find it useful, this Rbook is primarily developed for my students. Please report any errors or issues at https://github.com/Jadamso/Rbooks/issues.
Last updated: 05.02.2025