Applied Econometrics: A Big Data Experience for All

Data Science Methods and Applications in Economics, Finance and Marketing

Data science, big data and machine learning

The Minor Applied Econometrics provides a thorough introduction to econometric methods and data science techniques with an emphasis on how to implement and carry out the methods in empirical studies and how to interpret the results. The key steps of model formulation, parameter estimation, diagnostic checking, hypothesis testing, model selection and empirical analysis are given extensive attention throughout the different courses. Apart from the fundamentals of econometrics, much emphasis is given to how econometrics is carried out in different practical settings and empirical studies. Particular attention will be given to issues related to data science, big data and machine learning in the context of different disciplines including economics, finance and marketing.

Period 3.1 (September-October):

You take two courses:

“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 or empirical analyses in economics, finance and marketing. In particular, a review will be given of estimation and testing in the linear cross-sectional regression model. We will discuss the classical assumptions, and the consequences arising when these assumptions are not fulfilled. Throughout the course, the focus will lie on developing an intuition for state-of-the-art econometric concepts. A balance will be struck between theoretical derivations and empirical applications. Extensive use will be made of the statistical software Stata, both for in-class illustration and for hands-on exercises.

“Computational 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. You are introduced to autoregressive moving average (ARMA) models, autoregressive distributed lag (ADL) models, and error correction models (ECM). Furthermore, the course provides both theoretical and practical insight into parameter estimation in time series 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. We find a solution to this problem by taking a journey into the theory and practice behind unit-root test, cointegration tests and error-correction representation theorems.

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. More specifically, during the course consumer choice models, school assignment models, labor market models, search models and models in industrial organization are evaluated. 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 financial data and its properties, testing pricing efficiency and factor models, modelling volatility, risk management, 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 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 also expect to have a set of Case studies from Deloitte, De Nederlandsche Bank, Booking, and many others.

For more information about each course, please visit our study guide.

Admission and registration


  • bachelor students in econometrics, operations research and actuarial science
  • bachelor students within VU and SBE who want to develop a quantitative profile 
  • bachelor students in The Netherlands and abroad who want to learn about econometrics 
  • graduated Dutch HBO students in applied mathematics that plan to study MSc Econometrics 
  • students with deficiencies for starting MSc Econometrics (and its specialisation) at VU


VU student: You can register for the courses of the minor via VUnet from 15 July.
Other students: you can register as a secondary course student (bijvak) via More information about this procedure can be found on this website in Dutch or in English.

Questions about the programme

If you have detailed questions about the contents of the programme, please contact 

Julia Schaumburg

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