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.
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
They are licensed under BSD-3 and you are free to modify them in any way you wish.
You can either run the notebooks live in your browser or download them and run them locally.
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 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.