Advanced Econometrics I

In this course, you will gain a deep understanding of econometric theory, practice and inference. You will learn how to apply advanced econometric techniques in practice, extend available methods for particular applications and how to implement them in a matrix programming environment. Also you will learn to understand and derive their statistical properties.

Theory of Markets

In this course, you will study the microeconomic theory of perfect and imperfect competition. Learn under what conditions markets perform well as a means to organise economic activity (and under what conditions they do not).

Data Science Methods

In this course, you will cover the basic theory of multivariate data analysis and of statistical methods in data science. You will focus on the most relevant multivariate techniques, as well as their application to econometric data in computer lab sessions. We will introduce you to Python, NumPy and pandas, data scraping, cleaning and wrangling.

Advanced Econometrics II

In this course, you will build upon the general knowledge you acquired in Advanced Econometrics 1. You will gain a deep understanding of econometric theory, acquire the technical skills to conduct inference and be able to implement these techniques using software like MATLAB, R or Python.

CED 1: Learning, Stability and Chaos

In this course, you will analyse nonlinear dynamical systems with applications to economics and financial markets. You will apply complexity modelling to economics and finance and learn how to critically evaluate the literature on behavioural models with boundedly rational agents. Furthermore, you will learn how to interpret the results of computer-based simulated periodic and chaotic solutions.

Topics in Microeconometrics

In this course, you will discuss 8 recent empirical papers that apply microeconometric estimation techniques. These papers usually concern issues like individual choice behaviour in the labour and consumer markets. You will apply the techniques during the computer lab sessions with MatLab or R.

Mandatory electives: semester 1

Choose 1 out of 2 electives:

• Machine Learning for Econometrics
• Health Econometrics: Empirical Research

Mandatory electives: semester 2

Choose 2 out of 5 electives:

• Behavioural Macro and Finance
• CED 2: Optimal Control and Environmental Tipping
• Economic and Financial Network Analysis
• Environmental Econometrics: Empirical Research
• Financial Econometrics

Students who want to specialise in mathematical economics are recommended to take the courses Behavioural Marco and Finance and CED2: Optimal Control and Environmental Tipping.

The academic programme culminates in a thesis, which allows you to engage with state-of-the-art data analysis and statistical techniques. The Master’s thesis is the final requirement for your graduation. It is your chance to dive deep into a topic in your field of choice (track) that you are enthusiastic about, and allows you to do an independent research project. A professor of your track will supervise and support you in writing your thesis.

If you are a student of the Master's Econometrics and you have a record of academic excellence, a critical mind and an enthusiasm for applied research, then our Econometrics Honours programme is a great opportunity for you.

If you want to pursue a Master’s degree in Econometrics as well as in Mathematics, you can opt for one of our Double Degree Master’s programmes:

• Master's in Double Degree Programme in Econometrics and Mathematics. In combination with all specialisations of the Master's in Econometrics.
• Double Degree Master's programme Econometrics and Stochastics and Financial Mathematics. Only in combination with the specialisation Financial Econometrics of the Master's in Econometrics.

A specialisation track must be chosen when applying for the Master’s programme. However, track modifications are still possible until late October. The criteria for all tracks are identical and do not impact the likelihood of being accepted into the programme.

Our Master’s programme admits around 20 students per specialisation track. If you meet the entry requirements, you will always be accepted; this Master’s does not have a numerus fixus.

Most courses have one 2-3 hour lecture and one 2-hour tutorial per week. Generally students take 3 courses at a time, so count on about 12-15 contact hours per week.

Our preference is for in-person lectures. Certain sessions may be pre-recorded or follow a hybrid format. This entails preparing for Question and Answer (Q&A) sessions through video clips and readings, with subsequent discussions during meetings.

Attendance is usually not compulsory for lectures, but commonly for tutorials and other sessions. Students greatly benefit from being present and engaging in discussions with both the instructor and their classmates.

The majority of courses have a final written on-site exam. Most courses have additional assessment methods, including oral presentations, developing research proposals, conducting experiments and writing up results. Finally, some courses grade active participation. This is reflected by attendance and activity in tutorials and online assignments.