Unravel economic complexities with advanced mathematics
In the Econometrics track of the Econometrics Master's programme, you will dive into the advanced mathematics behind economic complexity. This track deals with modelling, estimation and testing of economic models using micro, macro and financial data as well as parametric and non-parametric techniques appropriate for cross section, time series and panel data.
- Prepare for a career in one of the most-sought after professions in the world today.
- Become fluent in the application of advanced mathematical and statistical methods.
- Specialise in econometric theory, panel data and micro- and financial econometrics.
Why choose the Econometrics track?
- Focus on the advanced mathematics behind economic complexity, beside the 4 general courses of the MSc Econometrics in your curriculum.
- You have access to up-to-date cases and learnings from the field of econometric theory, panel data and micro- and financial econometrics.
- After graduation, you have an excellent job prospect as e.g. risk management consultant or chief economist.
Apart from the 4 general courses of the full programme, you will follow 4 track-specific courses and electives.
Mandatory electives: semester 1
Choose 1 out of 3 electives:
- Asset Pricing
- CED 1: Learning, Stability and Chaos
- Machine Learning for Econometrics
Mandatory electives: semester 2
Choose 1 out of 11 electives:
- Behavioural Finance
- Economic and Financial Network Analysis
- CED 2: Optimal Control and Environmental Tipping
- Machine Learning in Finance
- People Analytics
- Quantitative Finance and Algorithmic Trading
- Real Estate and Alternative Investments
- Real Estate Finance
- Behavioural Macro and Finance
- Quantitative Models in Online Marketing
- Stochastic Calculus
In this course you will discuss about 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.
In this course you will cover econometric techniques that have been developed for the analysis of financial markets. You will apply these techniques to empirical data using Python and R and learn how to interpret the results from a financial perspective.
The manager of a football club can have great influence when it comes to winning prizes. With data driven analyses likely being the future, models that help clubs in managing their teams, become more and more relevant. We discuss an investigation that presents a management tool that can optimise a team's chances of fulfilling its sportive ambitions by adjusting their squad.
- Financial derivatives
- Trust frequency data of stock markets
- R&D investments and innovation
- The relation between travel costs and grocery shopping