A curriculum that builds deep expertise in econometric modelling and analysis
The Econometrics specialisation within the MSc Econometrics and Management Science offers a rigorous and comprehensive curriculum that prepares you to model, analyse, and solve real-world problems using advanced quantitative methods. You will gain a strong foundation in econometric theory and develop the skills to apply and extend these methods in practice.
Programme structure
The programme is structured across five blocks of eight weeks.
- Core courses cover a wide range of topics essential to modern econometrics, including microeconometrics and time series econometrics.
- Electives allow you to tailor your learning, with options ranging from machine learning to advanced macroeconomics.
- Seminar (Case Studies in Applied Econometrics) is a team-based research project where you apply your skills to real-world data under faculty supervision.
- Master thesis is written individually in the final blocks and may be theoretical or practice-oriented, often in combination with an internship or traineeship.
Curriculum overview
- 70% Courses
- 30% Case Studies in Applied Econometrics
The exact composition depends on your course selection.
In class
You will work on applied problems that reflect the complexity of modern economic and financial systems. For example:
How can we estimate the effect of a policy intervention or predict economic turning points?
You will explore topics such as modelling rare events, forecasting business cycles, and dealing with outliers and missing data.
In the seminar and thesis, you may work on cases such as:
- Estimating the market value of NBA players
- Predicting investor behaviour in financial markets
- Filtering long-run economic trends from real-time data
You will develop models, analyse data, and present findings that are both academically sound and practically relevant.
Study Schedule
The Take-Off is the introduction programme for all new students at Erasmus School of Economics. During the Take-Off you will meet your fellow students, get acquainted with our study associations and learn all the ins and outs of your new study programme, supporting information systems and life on campus and in the city.
This course deals with several (theoretical and applied) advanced topics in Microecometrics such as:
- methods of moments, general methods of moments
- linear, dynamic, and nonlinear panel data models
- heterogeneity and cross-section dependence in panel data
- duration models
- treatment effect evaluation
Bayesian Econometrics plays an important role in quantitative economics, marketing research and finance. This course discusses the basic tools which are needed to perform Bayesian analyses.
It starts with a discussion on the difference between Bayesian and frequentist statistical approach. Next, Bayesian parameter inference, forecasting and Bayesian testing is considered, where we deal with univariate models, multivariate models and panel data models (Hierarchical Bayes techniques). To perform a Bayesian analysis, knowledge of advanced simulation methods is necessary.
Part of the course is devoted to Markov Chain Monte Carlo sampling methods including Gibbs sampling, data augmentation and Monte Carlo integration. The topics are illustrated using simple computer examples which are demonstrated during the lectures.
Students choose one course (4 ec) from the listed courses:
- Asset Pricing (QF variant)
- Machine Learning in Finance
- Advanced Marketing Models
- Quantitative Risk Management
Students choose one course (4 ec) from the listed courses:
- Advanced Money, Credit and Banking
- Advanced Behavioural Economics
- Advanced Public Economics
- Advanced Macroeconomics
- Advanced Development Economics
- Experimental Economics
Companies currently have many sources of data available. In this course, we focus on multivariate relations in the data. This course deals with various multivariate statistical techniques to analyze such data sets. Examples are:
- Discriminant analysis/Classification
- Canonical correlation analysis
- Factor analysis
The course also contains the introduction to robust statistics and its interplay with the multivariate statistical methods.
This course covers advanced topics in time series econometrics and forecasting, including:
- state space methods
- regime-switching models
- forecasting with many predictors (factor models)
- forecast combinations
- structural breaks
- (structural) vector autoregressive models
In this course we will focus on nonparametric statistics and kernel methods.
We are familiar with methods such as maximum likelihood or linear regression. These are called parametric analysis as the data are assumed to come from a certain distribution (e.g., normal distribution) or have a certain relationship (e.g., linear relationship). Nonparametric analysis intends to uncover patterns in data without such assumptions. These methods are much more flexible and robust, but also come at a different cost.
Kernel is an essential concept in nonparametric statistics and helps facilitate many nonparametric methods. It also has many applications in machine learning and allows to extend many basic methods into more flexible versions, such as kernel PCA and kernel SVM.
Other possible topics for this class include a review of cross validation, bootstrapping, reproducing kernel Hilbert space and semiparametric statistics, depending on the level of the class. (To be decided later by the instructor)
The assessment will be based on the combination of two types of assignments. There will be a mathematical written assignment every week following the lecture, and there will be 3-4 practical assignments where you need to programme and apply the methods on data.
Students work in small groups on a specific theoretical or empirical econometric problem. Topics stem from international macro-, financial, and micro-econometrics, and may be provided by institutions, policy-makers and/or firms.
Each group has its own supervisor(s). Groups meet with their supervisor(s) on a weekly basis, to discuss the progress of the research and relevant issues and questions.
During the end of the term, each group writes a report about its research. A plenary session is scheduled at the end of the term, in which the research project is presented by members of the group and discussed by members of another group, followed by a plenary discussion.
Proposal for the Master thesis Econometrics and Management Science. This proposal can be used as a part of the Master thesis. There is no grade for this proposal.
The thesis is an individual assignment about a subject from your Master's specialisation. More information about thesis subjects, thesis supervisors and the writing process can be found on the Master thesis website
Disclaimer
This overview provides a general impression of the 2026-2027 curriculum. It is not the current study schedule. Enrolled students can find the most up-to-date version on MyEUR. Please note that minor changes may occur in future academic years.