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The importance of developing appropriate quantitative models and methods for risk-assessment, climate-impact scenarios, energy policy, house market prices, and big data analyses generally are well understood in the academic and professional worlds. The developments in new technologies for econometric modelling, analysis and forecasting of (big) data in finance, economics and business are moving forwards rapidly. In this summer school we will treat a number of these developments in much detail. In each case we start with the basics of the methodology and theory, we illustrate their use and their importance, we implement the basic methods in a computer lab, and we review the latest developments in the academic and professional literature. Given the interdisciplinary nature of the summer school, we start with a review of the basic methods and theory in each case. More specifically, we aim to treat the latest developments in univariate time series models, dynamic econometric models, volatility models, dynamic factor models, state space models, time-varying location and scale models, etc. The practical use of econometric methods in the context of specific applications are assessed in individual cases targeted towards the backgrounds of the participants.
The summer course welcomes (research) master students, PhD students and post-docs with a quantitative background and who are interested in learning state-of-the art econometrics and data science forecasting methods.
A formal background in quantitative studies (mathematics, statistics, econometrics, engineering, etc.) is required from students (at the level of a first year course in a Master study), but no formal background in Econometrics or Statistics will be assumed.
|Credits||Participants who joined at least 80% of all sessions will receive a certificate of participation stating that the summer school is equivalent to a work load of 3 ECTS. Note that it is the student’s own responsibility to get these credits registered at their university.|