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Statistics is about analysing data, econometrics is the application of statistical methods to economic data. Both disciplines involve the use of probability theory and computer simulations to establish properties of such methods.

Research and teaching at the Chair is mainly concerned with the statistical and econometric analysis of multivariate time series data.

Topics of interest, from both theoretical and applied perspectives, include structural breaks, forecasting, adaptive learning algorithms, and long memory. Empirical applications tend to focus on macroeconomic and financial data.

About us

Dr. Alexander Mayer
Dr. Alexander Mayer

Colloquium in Econometrics & Statistics

The aim of the colloquia is to provide a forum for discussion of research between WHU faculty and our guests. Colloquia take place once per term on average, with two to three talks per colloquium. Topics range from theoretical statistics to the empirical social sciences, with an emphasis on methodology and data analysis.

May 7, 2021:

Online-Seminar convened jointly with the Institute of Econometrics and Statistics at the University of Cologne

  • Andrew Harvey, University of Cambridge: Time series modeling of epidemics: leading indicators, control groups and policy assessment
  • Siem Jan Koopman, Vrije Universiteit Amsterdam: Forecasting in a Changing World: from the Great Recession to the COVID-19 Pandemic
  • Johannes Bracher, KIT: Collaborative forecasting of COVID-19: Assembling, comparing and combining short-term predictions
January 22, 2021:

Online-Seminar convened jointly with the Institute of Econometrics and Statistics at the University of Cologne

  • Morten O. Nielsen, Queen's University: Semiparametric tests for the order of integration in the possible presence of level breaks
  • Heiko Jürgen Rachinger, University of the Balearic Islands: Breaks in the level and persistence of time series
  • Tobias Hartl, University of Regensburg: Solving the unobserved components puzzle: A fractional approach to measuring the business cycle
October 30, 2019:
  • Michele Berardi, University of Manchester: Information aggregation and accumulation in prices
  • Jan Wenzelburger, University of Kaiserslautern: Learning in linear economic models with expectations feedback
  • Alexander Mayer, WHU: On consistent tests of strict exogeneity
February 21, 2019:
  • Carsten Trenkler, University of Mannheim: Robust structural impulse response inference in VARs and VECMs with conditional heteroskedasticity of unknown form
  • Otilia Boldea, Tilburg University: Bootstrapping structural change tests
December 5, 2018:
  • Michael Vogt, University of Bonn: Multiscale inference and long-run variance estimation in nonparametric regression with time series errors
  • Roderick McCrorie, University of St. Andrews: The exact asymptotic first-order bias in least squares estimation of the AR(1) model under a unit root
  • Mustafa Kilinc, WHU: Detecting outliers and locations shifts under long-memory stationary errors
April 24, 2018:
  • Bent Nielsen, University of Oxford: Asymptotic theory of outlier detection algorithms for linear time series regression models
  • Philipp Sibbertsen, Leibniz University Hannover: The periodogram of spurious long memory processes
March 6, 2018:
  • Mathias Hoffmann, University of Zürich: Domestic bank dependence and risk sharing in the eurozone before and after the great recession
  • Michael Evers, University of Hohenheim: Solving nonlinear expectations models by approximating the stochastic equilibrium system
  • Alexander Mayer, WHU: A three-step estimator for macroeconomic learning models

Economics Brown Bag Seminar

The aim of the Economics Brown Bag Seminar is to provide an informal forum for junior and senior faculty of the Economics Group at WHU to present research ideas or their current work. Interested speakers are welcome to contact our office.

May 25, 2019, Emanuel Holler
  • Minimum resale price maintenance and upstream collusion: Empirical evidence from the German coffee cartel


February 18, 2019, Markus Kempers
  • Panel data analysis of mergers and acquisitions in family businesses - some open issues
November 14, 2017, Dimitry Smirnow
  • Application of Advanced Analytics for Proactive Retention Management in Noncontractual Settings
September 26, 2017, Benedikt Walter
  • Do Bilateral Investment Treaties Attract Foreign Direct Investment? The Role of Investor-State Dispute Settlement Provisions
September 12, 2017, Frederik Neugebauer
  • Measuring the Effect of ECB’s Asset Purchase Announcements
December 8, 2015, Alexander Mayer
  • On the sense and nonsense of time series econometrics
September 1, 2015, Yiqiao Sun
  • Demography and sector-level price development
March 3, 2015, Patrick Huber
  • Real life challenges in regression analysis with empirical data
February 20, 2015, Alexander Mayer
  • Some aspects of estimating large heterogneous panel data moldes
February 13, 2015, Prof. Dr. Michael Massmann
  • Estimating a model of Eurozone inflation with adaptive learning

RMSE Workshop

The Rhenish Multivariate Time Series Econometrics (RMSE) Workshops are medium-scale meetings of junior and senior researchers for the discussion of work-in-progress in time series analysis. Participants tend to come from universities along the Rhine, broadly defined, and number around 30.

Previous workshops:

  • 2018: WHU – Otto Beisheim School of Management
  • 2017: Erasmus University Rotterdam
  • 2015: University of Cologne
  • 2013: Tinbergen Institute
  • 2011: University of Bonn
  • 2010: Vrije Universiteit Amsterdam
  • 2008: Vrije Universiteit Amsterdam

DFG Project

The effect of structural breaks on inference with stochastic processes subject to long memory

The project is motivated by Bertram, Kruse & Sibbertsen (2013), who find that correlations of American stock returns show long-term dependence as well as that there are structural breaks in the observed time series, possibly occurring at the same time points. This empirical regularity raises a number of questions. On the one hand, the long-term dependence structures themselves may possibly be due to structural breaks in the mean of the series. Therefore, it is necessary to derive an algorithm that can be used to test for structural breaks in multivariate time series and that is robust to long memory. On the other hand, the timing of the structural breaks suggests that a co-breaking relationship between the time series could exist. However, the co-breaking test proposed by Hendry & Massmann (2007) is based on a regression model with independent disturbances, and currently no co-breaking test is available for long-memory data structures. A second goal of the project is therefore to develop such a test in order to investigate the interaction between co-breaking and long-term dependence. In order to have a coherent modelling strategy for empirical application, it is also necessary to gain an understanding of seemingly long memory and thus of the interplay between cointegration and co-breaking. A further objective of the project is therefore to investigate which properties distinguish structural break processes, which have autocorrelation structures similar to those with long memory, from processes with true long memory. One such property is the validity of functional central limit theorems. In addition, although the autocorrelation function of a structural break process can indeed show hyperbolic decay, it converges to a positive constant and not to zero, as in processes of true long memory. The findings are then generalised to the case of multivariate time series systems. The results of Leschinski & Sibbertsen (2017) suggest that co-breaking leads to an apparent fractional cointegration. A final goal of the project is to apply the newly derived statistical methods to financial market data in a detailed empirical study to investigate both the impact of co-breaking on portfolio selection and the accuracy of forecasts of realized correlations.

This project is carried out jointly with the Institute of Statistics at Leibniz University Hannover.

Teaching and Supervising


  • "Causal Inference and Reasoning" (Bachelor Program)
  • "Time Series Analysis and Machine Learning" (Bachelor Program)
  • "Topics in Advanced Econometrics" and "Causal Inference" (Doctoral Program)


Bachelor's and Master's theses: 

If you are a WHU student and are interested in writing your thesis at the chair, feel free to contact our office in order to make an appointment. You are strongly encouraged to suggest yourself a thesis topic in the form of a 1-page research proposal. Any subject in the fields of statistics, probability theory, econometrics or an empirical social science is potentially suitable, be it theoretical, empirical or computer simulation-based. Examples of theses supervised in the past are:

  • "The impact of UN peace keeping missions on the economic development of developing countries"
  • "Identification issues in cointegration analysis"
  • "Predicting time series based on deep learning algorithms"
  • "Artificial Neural Networks and their Application to the Prediction of Financial Time Series"
  • "Forecasting macroeconomic time series using principal components"
  • "Diamond demand forecasting via online activities"


Doctoral theses: 

If you hold a Bachelor's and a Master's degree and are interested in writing a doctoral thesis at the chair as an external PhD student you are welcome to send to our office a 5-page research proposal, a CV including the names of two referees, and copies of relevant certificates and transcripts. All documents must be in either German or English. There is a strong preference for thesis topics within the realm of multivariate time series econometrics, broadly defined.

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