Study programme


The master’s specialisation consists of seven core courses; the seminar course Case Studies in Applied Econometrics, and a master’s thesis distributed over five blocks of eight weeks each.

The core courses (among which Microeconometrics and Time Series Econometrics for Macroeconomics) help students to get acquainted with a wide range of topics that are essential for econometrics. Most of the courses consist of assignments, which can be individual or in small groups and a final exam. During the seminar course Case Studies in Applied Econometrics small groups of students undertake an applied research project under supervision of a staff member.

You have the opportunity to choose two electives from nine courses, divided in two groups of electives. From each group of electives, you can pick one course. The electives vary from Machine Learning to Advanced Macroeconomics.

The last two blocks of the programme are devoted to the master’s thesis, which can be theoretically oriented or practically oriented. Practically oriented theses are usually combined with an internship or traineeship. The thesis is written individually under close supervision by one of the staff members of the Econom

The curriculum consists of:

  • 70% courses
  • 30% case studies in applied econometrics

In class

During the courses topics include:

  • Modeling and forecasting rare events
  • Estimating business cycles and predicting transitions between good times and recessions
  • How to deal with outliers and missing data in empirical analysis
  • Estimating the effect of a certain treatment, such as a policy intervention

The topics of cases for the research projects and master thesis are broad and include:

  • How to optimally filter the long-run economic trend from real-time data
  • Estimating the market value of NBA players
  • Predicting whether a certain investor will increase the investment in a particular fund

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.

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.

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

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 choose one course (4 ec) from the listed courses:

  • Asset Pricing (QF variant)
  • Advanced Marketing Models     
  • Machine Learning in Finance
  • Quantitative Risk Management

Students choose one course (4 ec) from the listed courses:

  • Advanced Behavioural Economics
  • Advanced Development Economics
  • Advanced Macroeconomics
  • Advanced Money, Credit and Banking
  • Advanced Public Economics
  • Experimental Economics

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

The overview above provides an impression of the curriculum for this programme. It is not an up-to-date study schedule for current students. They can find their full study schedules on MyEUR.

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