“Introduction to Econometrics” (6 ECTS, for non-econometrics students):
- Coordinator: dr. Julia Schaumburg.
- Contact hours per week : 2 hours theory classes and 4+ hours class work.
- This course is an introduction to modern econometric techniques, which enable you to conduct methodological and empirical analyses in economics, finance and marketing. We discuss the linear regression model and its assumptions, and the consequences that arise when these assumptions are not fulfilled. Furthermore, an introduction to panel data analysis is given. Overall, a balance is struck between theoretical derivations and empirical applications.
“Bayesian Econometrics” (6 ECTS, for econometrics students):
- Coordinator: dr. Lennart Hoogerheide.
- Contact hours per week : 4 hours classes + 2 hours computer room tutorials.
- This course about Bayesian Econometrics in the minor Applied Econometrics is targeted at Bachelor Econometrics students and Bachelor students with different backgrounds who have already had an introduction to programming and econometrics/statistics. The objective is to acquaint you with Bayesian statistics and applications thereof to econometric problems, using advanced computational methods. This course will cover Bayesian statistics where the topics include the prior and posterior density, Bayesian hypothesis testing, Bayesian prediction, Bayesian Model Averaging for forecast combination. Several models will be considered, including the Bernoulli/binomial distribution for binary data, the Poisson distribution for count data and the normal distribution. Obviously, attention will be paid to the Bayesian analysis of linear regression models. Also applications to simple time series models will be considered. An important part of the course is the treatment of simulation-based methods such as Markov chain Monte Carlo (Gibbs sampling, data augmentation, Metropolis-Hastings method) and Importance Sampling, that are often needed to compute Bayesian estimates and predictions and to perform Bayesian tests.
“Introduction to Time Series” (6 ECTS, for both econometrics and non-econometrics students):
- Coordinator: dr. Francisco Blasques.
- Contact hours per week : 2 hours theory classes and 4+ hours class work.
- This course covers both theoretical and practical aspects of time series econometrics, including the analysis of stationary and non-stationary stochastic processes in economics and finance. Furthermore, the course provides both theoretical and practical insight into parameter estimation in time series models and the use of these models for forecasting, testing for Granger causality, and performing policy analysis using impulse response functions. Finally, you are introduced to the fundamental problem of spurious regression in time series analysis.
Period 3.2 (November-December):
You choose two of three courses:
“Empirical Economics” (6 ECTS, for all)
- Coordinator: prof. dr. Bas van der Klaauw.
- Contact hours per week : 2 hours theory classes and 4+ hours class work.
- This course first provides an overview on microeconometric techniques to estimate causal effects. In particular, the potential outcomes framework is discussed and within this framework policy relevant treatment effects are defined. Next, more structural economic models are presented and empirical analyses of these models are discussed. During the course, there will be a theoretical discussion, presentation of empirical studies and you have to work with data, “big data”.
“Empirical Finance” (6 ECTS, for all)
- Coordinator: dr. Norman Seeger.
- Contact hours per week : 2 hours theory classes and 4+ hours class work.
- This course covers topics such as financial data and its properties, tests for pricing efficiency and factor models, volatility modelling, risk management, and continuous time finance. A mixture of academic papers and practical applications is used to study how econometric methodology is employed to facilitate financial decision making and to extract information from financial market data. We adopt various econometric methods based on regression models, generalised conditional heteroskedasticity (GARCH) models, historical simulation, and Monte Carlo simulation.
“Empirical Marketing” (6 ECTS, for all)
- Coordinator: dr. Francesca Sotgiu.
- Contact hours per week : 2 hours theory classes and 4+ hours class work.
- This course focusses on quantitative methods for marketing and for empirical research in consumer behaviour. In particular, we discuss how to build models to support marketing decisions and how to adopt data science methods to investigate market behavior and the impact of marketing activities such as advertising, pricing, promotions and distribution. The econometric methods that are employed include regression, multivariate statistical analysis, limited dependent variable models, panel data models, pooled regressions, forecasting methods, and trend extraction.
Period 3.3 (January):
“Case studies in Data Science” (6 ECTS, for both econometrics and non- econometrics students):
- Coordinator: prof. dr. Siem Jan Koopman.
- Contact hours per week : 2 hours theory classes and 4+ hours class work.
- Initial meeting, follow-up meeting(s) with supervisors at the premises of the organisation or firm, online support by coordinator.
- Case studies are carried out by teams of students, possibly coming from different study backgrounds. You must write a Case Report and present your results to groups of teachers, professionals and fellow students. The groups compete to come up with the best study for a data science analysis. This year we expect to have a set of case studies from Deloitte, De Nederlandsche Bank, Booking.com, and many others.
For more information about each course, please visit our
study guide.
