**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

# A Primer in Econometric Theory¶

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

## About¶

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.

## Samples¶

## Lecture Slides¶

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

- Lecture 1 (
`PDF`

|`LaTeX`

) - All other PDFs and source files are available from GitHub

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

## Source Code¶

The code in the book is written in a mixture of R, Python and Julia and organized into Jupyter notebooks.

You can either

- run the notebooks live in your browser or
- download them and run them locally

Details follow.

### Run the Notebooks Live¶

This is good option for experimenting. You can run, edit and rerun the code in your browser without having to install any software. However, you won’t be able to save your changes.

### Download and Run Locally¶

The first step is to get hold of the notebooks themselves. You can do this by cloning the GitHub repository or just grabbing the zip file.

The next step is to install some combination of R, Python and Julia, depending on what notebooks you want to execute. The notebooks themseves tell you which language they use.

Whatever languages you want to use, you will need Jupyter. This comes bundled in the Anaconda Python distribution, which provides Jupyter, Python and some great scientific tools for working with Python.

I highly recommend it.

You are now ready to run any Jupyter notebooks that contain Python code. To run the notebooks with Julia code you need to install Julia and the IJulia package. These instructions provide more information.

To work with R in Jupyter notebooks try reading this.

Now start Jupyter and point it at the notebooks you’ve just downloaded and you’ll be in business.