📖Literature

This page provides some useful references with respect to different areas that are important for studying econometrics. The intention is not that you study all of these before you start the minor Applied Econometrics. Instead, it is intended to

• Give you the possibility to read up on (and practice) particular topics you feel less certain about.

• Check whether your pre-requisite knowledge matches with the expected level for this minor program.

In addition to the sources below, you might also want to visit the website of dr. K. Moussa, who is a teacher in the minor programme. He has set up notes on programming, probability theory and statistics, which are tailor-mode for this study.

Statistics

The following books are suitable as an introduction in the fields of Probability Theory and Statistics.

• The first reference provides a very basic introduction needed to enter an introductory course in econometrics.

• Ideally, more in-dept knowledge is required, and the second reference provides exactly that background (suitable for the regular track of the minor).

• The third reference is very advanced (used during the BSc EOR and thus appropriate for the technical track of the minor).

References

Basic

Stock, J.H. and Watson, M.W. (2019). Introduction to Econometrics. 4th edition.

--> Chapter 1-3.

Intermediate

Rice, J.A. (2007). Mathematical Statistics and Data Analysis. Cengage Learning.

--> Chapter 1-6, 8, 12.

Advanced

Casella, G. and Berger, R.L. (2008). Statistical Inference. International Edition of the 2nd revised edition, Cengage Learning.

--> Chapter 1-10.

Econometrics

The following books are suitable as an introduction to the field of Econometrics. It is not mandatory to have econometrics knowledge when following the regular track of the minor (but it might be beneficial to know some of the basics). In case you have already had econometrics classes before, you can check your pre-requisite knowledge here.

• The first two books are often used textbooks for introductory econometrics. In the BSc EOR, they are used in the second-year courses Econometrics I and II and therefore provide appropriate references.

• The last two books combine econometrics and linear algebra, because the linear regression model is considered in the context of matrix algebra. The books are quite thin and cover the basics, which makes them appropriate for self-study.

References

Basic/intermediate (no matrix algebra):

Stock, J.H. and Watson, M.W. (2019). Introduction to Econometrics. 4th edition.

--> Chapter 4-5 and 18 (simple linear regression)

--> Chapter 6-7 (multiple linear regression)

Wooldridge, J. (2013). Introductory Econometrics. A Modern Approach, 5th international edition.

--> Chapter 1-5

Basic/intermediate (matrix algebra):

J.R. Magnus (2017). Introduction to the Theory of Econometrics. VU University Press.

--> From sixth printing onwards, the book has video links

J.R. Magnus and S. Telg (2021). Mastering Econometrics: Exercises and Solutions. VU University Press.

--> Book has theoretical and empirical exercises with solutions. Support is offered for RStudio.

These books can be bought as a set with discount at VU University Press.

Linear Algebra

References

Lay, D.C., Lay, S.R. and McDonald, J.J. (2021). Linear Algebra and its Applications. 5th edition, Pearson Global Edition, ISBN-139781292092232

Analysis

References

Sydsaeter K., Hammond P., Strom A. and Carvajal, A. (2021). Essential Mathematics for Economic Analysis. Fifth edition. Pearson.

Programming

The two main programming languages used in the minor Applied Econometrics are Python and R. You do not have to be an expert in both, so it is sufficient to focus on one of them.

• In the regular track, you will get a (short) introduction to programming. It is not mandatory to have programming pre-knowledge, but it can be very useful.

• In the technical track, it is assumed that you know how to program in at least one programming language (e.g., R, Pyton, Matlab, Julia, C++, Java).

Note that you most importantly focus on getting to know the basics of programming. This involves defining variables, expressions, if-statements, for-loops, etc. For Python, Chapter 1-5 of the online book "How to think like a computer scientist" is a good indication of useful programming knowledge for econometricians. Replicating the results in those chapters might be a nice way to learn programming step-by-step.

Another option is to follow an online course; we list a few below. It suffices to follow one on the basics of programming, see previous paragraph for some crucial topics. There is no need to follow extremely elaborate courses on complex topics (the course selection that these websites offer is sometimes quite overwhelming).

Online courses

Python

MIT OpenCourseWare "Introduction to Computer Science and Programming in Python"

QuantEcon

R

Codeacademy

Note that courses for both programming languages are also available online using popular platforms such as Datacamp and Coursera. Usually these costs money however (a discount may be obtained by showing your university enrollment).

High school math

Do you feel like your math skills are a bit rusty?

• Take part in the summer preparation programme that is also followed by prospective students in the bachelor Econometrics. You can sign up for it using the link https://cloud.sowiso.nl/enroll/ERxdsLv7. Costs are €26.25.

• Khan Academy offers a free platform with theory and practice quizzes.

• K. Sydsaeter, P. Hammond, A. Strom, A. Carvajal (2021). Essential Mathematics for Economic Analysis. Sixth edition. Pearson. (Book has a lot of exercises)