Applied Econometrics: A Big Data Experience for all

Overview courses

• Two tracks

  The minor consists of two tracks: a regular and a technical track. The regular track is intended for students who up until then have successfully completed their first courses in (introductory) mathematics and statistics. The technical track is tailored towards current students in the BSc EOR and BSc EDS (or in strongly related quantitative fields). Have a look at this preparation website to see whether your background fits this minor programme.

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

  • 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.

• Fundamentals of Time Series Econometrics (period 1, 6 EC, regular track)

  • Contact hours per week: 4 hours lecture, 2 hours tutorial (computer practical)
  • This course provides students in the regular track an introduction to the most important concepts of time series econometrics. It covers the main fundamental issues regarding the analysis of stationary and non-stationary stochastic processes in economics and finance. Topics such as parameter estimation, forecasting, testing for Granger causality, performing policy analysis using impulse response functions and spurious regression will be discussed. The course focuses on theoretical aspects, but practical aspects such as the implementation of methods and interpreting estimation results based on real-life examples also play a central role.

• Time Series and Dynamic Econometrics (period 1, 6 EC, technical track)

  • Contact hours per week: 2 hours theory classes and 4+ hours class work
  • This course introduces theoretical and practical principles of time series econometrics to students in the technical track of the minor. The focus lies on the analysis of stationary and non-stationary stochastic processes in economics and finance. The first part of the course considers stationary time series based on the Box-Jenkins methodology and extensions thereof. The second part focuses on how to test for possible non-stationarity and discusses possible consequences of dealing with such time series. Important topics covered in this course are parameter estimation in time series models (maximum likelihood and least squares) and its asymptotic properties, the use of these models for forecasting, testing for Granger causality, performing policy analysis using impulse response functions, unit root testing, cointegration and spurious regression in time series analysis.

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

  • 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 and finance. 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)

  • 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)

  • 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)

  • 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. 

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

  • 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. The projects are provided by companies, organisations, or research institutions. Teams write a case report and present their results to groups of teachers, professionals, and fellow students. Effective teamwork is required to analyse a complex and typically “big data” set, and to provide solutions and advice for the problem statements at hand.