Random Quote: And in order to do good to others he would have needed, besides the principles which filled his mind, an unprincipled heart–the kind of heart that knows no general cases, but only of particular ones, and has the greatness of small actions. – Boris Pasternak

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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.

Chapters

• Table of Contents
• Chapter 1: Introduction

Part I: Background

• Chapter 2: Vector Spaces
• Chapter 3: Linear Algebra and Matrices
• Chapter 4: Foundations of Probability
• Chapter 5: Modeling Dependence
• Chapter 6: Asymptotics
• Chapter 7: Further Topics in Probability

Part II: Foundations of Statistics

• Chapter 8: Estimators
• Chapter 9: Properties of Estimators
• Chapter 10: Confidence Intervals and Tests

Part III: Econometric Models

• Chapter 11: Regression
• Chapter 12: Ordinary Least Squares
• Chapter 13: Large Samples and Dependence
• Chapter 14: Regularization

Part IV: Appendix

• Appendix
• Bibliography

Lecture Slides

Thanks to the work of Akshay Shanker, a full set of lecture slides is 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.

Source Code

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