# Using Python for Introductory Econometrics

## Using Python for Introductory Econometrics

Welcome to the companion web site to the book

Using Python for Introductory Econometrics
by Florian Heiss and Daniel Brunner
ISBN: 979-8648436763

It can be

### Content and Approach

This book introduces the popular, powerful and free programming language and software package Python with a focus on the implementation of standard tools and methods used in econometrics. Unlike other books on similar topics, it does not attempt to provide a self-contained discussion of econometric models and methods. Instead, it builds on the excellent and popular textbook "Introductory Econometrics" by Jeffrey M. Wooldridge. Some other editions and versions work as well, see below. It is compatible in terms of topics, organization, terminology and notation, and is designed for a seamless transition from theory to practice. Topics include:

• A gentle introduction to Python
• including the modules Pandas, NumPy, SciPy and Matplotlib
• Simple and multiple regression in matrix form and using black box routines
• Inference in small samples and asymptotics
• Monte Carlo simulations
• Heteroscedasticity
• Time series regression
• Pooled cross-sections and panel data
• Instrumental variables and two-stage least squares
• Simultaneous equation models
• Limited dependent variables: binary, count data, censoring, truncation, and sample selection
• Formatted reports and research papers using Jupyter Notebooks combining Python with Markdown or LaTeX

The chapters have the same names and cover the same material as the respective chapters in Wooldridge’s textbook. Assuming the reader is familiar with the concepts discussed there, this book explains and demonstrates how to implement everything in Python and replicates many textbook examples. We also open some black boxes of the built-in functions for estimation and inference by directly applying the formulas known from the textbook to reproduce the results. Some supplementary analyses such as Monte Carlo simulations provide additional intuition and insights.

The book is designed mainly for students of introductory econometrics who ideally use Wooldridge’s “Introductory Econometrics” as their main textbook. It can also be useful for readers who are familiar with econometrics and possibly other software packages, such as Stata. For them, it offers an introduction to Python and can be used to look up the implementation of standard econometric methods.

All computer code used in this book can be downloaded to make it easier to replicate the results and tinker with the specifications.