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.
The minor also provides a track for current Bachelor Econometrics & OR students (or those from related studies) with options for advanced methodological courses, including Bayesian Econometrics, an advanced Case Study and an internship.

Skills for the job market

Econometricians are frequently hired, for example, as data scientists, quantitative researchers, data modelers, and quantitative analysts. But nowadays, as large data sets are collected nearly everywhere, the knowledge of econometric tools and methods is becoming an increasingly valuable asset also for economists, business executives, engineers, asset managers, consultants, risk managers, and marketing specialists, just to name a few. Students who successfully complete the minor are also invited to consider continuing with the M.Sc. in Econometrics, provided some additional prerequisites are met (see “Related Master’s Programs: Econometrics” below).
For current Bachelor Econometrics & OR students (or those from related studies), the minor offers insights into more theoretical, methodological and empirical aspects of econometric modelling in areas such as finance, economics and marketing, thereby providing technical as well as practical skills that are complementary to those acquired in the Bachelor program.

The minor consists of two tracks: a regular and a technical track. The regular track contains five mandatory courses. The technical Each track consists of obligatory and elective courses. Also, an internship is possible (in both tracks); in that case one of the courses in period 2 plus the period 3 course will be cancelled).

Computational Methods in Econometrics (period 1, 6 EC, technical track):

  • Coordinator: S J Koopman
  • Contact hours per week: 4 hours lectures, 2 hours tutorial
  • In this course we discuss numerical and simulation-based methods and their use in econometrics and data science. In the first part, we review numerical methods for optimization, Monte Carlo integration and matrix computation. We show how these methods are used for the estimation of parameters in discrete and nonlinear models. In the second part, we investigate properties of estimators, test statistics and model residuals, using simulation studies. In particular, we simulate distributions of parameter estimates under different data generation processes, distributions of test statistics used in unit-root tests, goodness-of fit measures in spurious regressions, and model selection criteria such as the Akaike information criterion. Finally, we use simulations to verify the accuracy of diagnostic tests related to normality and heteroscedasticity.

Introduction to Time Series and Dynamic Econometrics (period 1, 6 EC, both tracks):

  • 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.

Introductory Econometrics for Business and Economics (period 1, 6 EC, regular track):

  • 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 for Business and Economics (period 2, 6 EC, technical track):

  • 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.

Empirical Economics (period 2, 6 EC, both tracks):

  • 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 (period 2, 6 EC, both tracks):

  • 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 simulatio

Practical Case Study: Real-life Modelling in Econometrics and Data Science (period 3, 6 EC, both tracks):

  • 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 organization 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,, and many others.

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


  • 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.

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

Julia Schaumburg

Related master's programmes

Samenvatting Applied Econometrics: A Big Data Experience for All




1 semester (30 EC)


1 September


Economie, Recht en Bestuur
Informatica, Wiskunde en Bedrijf


School of Business and Economics