**Random Quote:** *The goal of the scientist is to comprehend the
phenomena of the universe that he observes around him. To prove that he
understands he must be able to predict. To predict quantitatively one
must have a mechanism for producing numbers, and this necessarily
entails a mathematical model.* – Richard Bellman

This is the homepage for my graduate level econometric theory text, published by MIT Press.

The following is from the preface to the book:

This is a quick course on modern econometric and statistical theory, along with the underlying ideas from probability and linear algebra that budding econometricians should know. The focus is on foundations and general principles. Although it was written to teach from, there are many solved exercises, making the text well suited to self-study. Exercises, worked examples and sample code are used to reinforce ideas.

- Table of Contents
- Chapter 1: Introduction
- Chapter 2: Vector Spaces
- Chapter 3: Linear Algebra and Matrices
- Chapter 8: Estimators
- Chapter 11: Regression
- Chapter 14: Regularization

Thanks to the work of Akshay Shanker, a full set of lectures slides are available from GitHub – both PDF and source (TeX)

They are licensed under BSD-3 and you are free to modify them in any way you wish.

The code in the book is written in a mixture of R, Python and Julia. It is organized into Jupyter notebooks, which you can get by cloning the GitHub repository or just grabbing the zip file.

The next step is to install Jupyter, which comes bundled with the Anaconda Python. Then, if you want to run the R and Julia code, you’ll need the appropriate kernels. Search for documentation on how to run R and Julia code in a Jupyter notebook.