Introductory Econometrics in Python
Preface
This book is the result of several years of teaching introductory econometrics at the undergraduate level. While there is no shortage of excellent resources that explain econometric theory, many students find it challenging to connect formal concepts with real empirical work. This document was written with the goal of bridging that gap.
The conceptual foundations draw heavily from Introductory Econometrics: A Modern Approach by Wooldridge (2020) and Introduction to Econometrics by Stock and Watson (2020). These works have been instrumental in shaping the presentation and scope of the material, and full credit is given to their authors for the theoretical framework.
The central idea of this text is to combine econometric concepts with practical applications in Python in a way that is directly integrated into classroom instruction. As students are introduced to each concept, the book is used during class sessions to examine the corresponding code and to run empirical applications using real-world data. This approach allows students to engage with both theory and implementation simultaneously, fostering a more intuitive understanding of econometric methods and their practical purpose.
The book focuses primarily on cross-sectional data and time series analysis, which together form the core of most introductory econometrics courses and a large share of applied research. The emphasis throughout is on interpretation, implementation, and critical thinking, rather than on purely mechanical computation.
Python plays a central role in every chapter. As an open-source programming language with a rapidly growing ecosystem for data analysis, Python provides an accessible and powerful platform for applied econometrics. All examples in the book rely on freely available tools, encouraging reproducibility, transparency, and good empirical practices. Students are exposed early on to workflows that mirror those used in academic research and professional settings, including data cleaning, estimation, visualization, and replication.
For any questions or suggestions, feel free to contact me at is.guizar@gmail.com.