πŸ“ˆ
Preparation Minor Applied Econometrics
  • πŸ““Preparation module
  • πŸ“‹Table of contents
  • πŸ“šImportant concepts
    • πŸ“‰What is econometrics?
    • ♦️Univariate random variables
    • ♠️Multivariate random variables
    • πŸ§ͺEstimation and inference
    • ♾️Asymptotics
  • πŸ“–Literature
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Literature

PreviousAsymptotics

Last updated 9 months ago

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 , 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.

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

--> Chapter 1-3.

Intermediate

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

Advanced

--> Chapter 1-10.

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):

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

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

--> Chapter 1-5

Basic/intermediate (matrix algebra):

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

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

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

References

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

References

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).

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

R

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

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 "" 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.

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

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 . Costs are €26.25.

offers a free platform with theory and practice quizzes.

(Book has a lot of exercises)

the website of dr. K. Moussa
Stock, J.H. and Watson, M.W. (2019). Introduction to Econometrics. 4th edition.
Rice, J.A. (2007). Mathematical Statistics and Data Analysis. Cengage Learning.
Casella, G. and Berger, R.L. (2008). Statistical Inference. International Edition of the 2nd revised edition, Cengage Learning.
Stock, J.H. and Watson, M.W. (2019). Introduction to Econometrics. 4th edition.
Wooldridge, J. (2013). Introductory Econometrics. A Modern Approach, 5th international edition.
J.R. Magnus (2017). Introduction to the Theory of Econometrics. VU University Press.
J.R. Magnus and S. Telg (2021). Mastering Econometrics: Exercises and Solutions. VU University Press.
VU University Press
Sydsaeter K., Hammond P., Strom A. and Carvajal, A. (2021). Essential Mathematics for Economic Analysis. Fifth edition. Pearson.
How to think like a computer scientist
MIT OpenCourseWare "Introduction to Computer Science and Programming in Python"
QuantEcon
Codeacademy
Datacamp
Coursera
https://cloud.sowiso.nl/enroll/ERxdsLv7
Khan Academy
K. Sydsaeter, P. Hammond, A. Strom, A. Carvajal (2021). Essential Mathematics for Economic Analysis. Sixth edition. Pearson.
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