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This programme encompasses the research of the Econometric Institute (60th anniversary in 2016). The programme has two main research themes: Econometrics and Management Science.
The aim is to perform high-quality applied research at the forefront of developments in the field. To achieve this goal the programme has to undergo periodic stages in which substantial renewals take place with regard to methodology and the field of applications. This idea has also been applied in the recent years. An important focus for the coming years is business analytics.
Sustained by the 'big data frenzy', methods from Machine Learning have been cross-fertilizing a variety of fields.
This workshop brings together a group of leading international researchers in Robust Statistics and Econometrics.
The research in this theme focuses on data-driven econometric research using advanced statistical methods and techniques. The goal of this research is to push the state of the art in econometrics techniques, to provide economic agents, including policy-makers, firms and investors, with quantitative support to make the best possible decisions. More specifically, the mission of the research area is to develop sound methodological procedures for different key aspects of such decision-making problems, including data collection, econometric model specification, parameter estimation, model evaluation, and forecasting. The research is typically on the cutting edge in terms of existing econometric techniques. The main fields of application are:
- Macroeconomics (real-time data & expert forecasts);
- Finance (high frequency data);
- Marketing (databases of firms & forecasts by managers, internet data).
It is expected that the amount of data that will become available for business and research will continue to increase in the near future, e.g. due to the expected 100 billion devices that will be connected to the ‘Internet of Things’ in 2020 and will provide an almost unimaginable amount of heterogeneous data on a real-time basis. At the same time, the information content per observation will probably decrease. In such an environment new econometric methods and models will be needed to extract signals from the noisy data. Advanced computational techniques will be needed to process the large quantities of data. The econometrics area intends to keep on playing an important role in developing advanced econometric methods and models to prevent a data deluge.
The aim of this research theme is to be at the academic forefront of the developments in transportation, logistics and supply chain management in interaction with business intelligence systems, and to make major contributions to both management science and management practice. Particular topics of interest are:
- Service, reverse and green logistics: service logistics concerns all logistical activities after a sale has been completed and concerns provision of spare parts and maintenance. Reverse logistics concerns all logistic activities to recover value from discarded products. Finally green logistics concerns all environmental aspects related to logistics;
- Transportation optimisation: the goal is to improve the performance of passenger and cargo transportation systems, in particular Dutch Railways and Port of Rotterdam;
- Health care optimisation: here we develop models and methods to increase efficiency in health care institutions and to increase the quality of care;
- Business intelligence systems: This concerns the application of information and communication technologies and advanced computational methods for improving decision making in business economic domains.
While our research is often motivated by real-world applications, its focus is on the development of new analytic approaches to advance science and society. This usually entails building new mathematical models and/or developing new solution methods or methodologies. In our view, each of the topics mentioned above will remain relevant and challenging in the coming years.
- Dollevoet, T.A.B., Huisman, D., Schmidt, M.E. & Schöbel, A. (2012). Delay Management with Rerouting of Passengers, Transportation Science, 46(1), 74-89. Paap, R., Segers, R. & Dijk, D.J.C. van (2009). Do leading indicators lead peaks more than troughs?, Journal of Business and Economic Statistics, 27(4), 528-543.
- Paap, R., Segers, R. & Dijk, D.J.C. van (2009). Do leading indicators lead peaks more than troughs?, Journal of Business and Economic Statistics, 27(4), 528-543.