Modern Topics in Time Series Analysis

The summer school provides a PhD-level introduction to a range of modern topics in time series analysis taught by a selection of top researchers in Europe.

Topics covered (in alphabetical order):
Bayesian Methods for (Macro and Financial) Time Series Analysis (Gregor Kastner, University of Klagenfurt)
Dynamic Factor Models (Manfred Deistler, Vienna University of Technology)
Econometric Modelling of Climate Change (Eric Hillebrand, Aarhus University)
Functional Time Series Analysis (Siegfried Hörmann, Graz University of Technology)
Hidden Markov Models (Timo Adam, Bielefeld University)
Nonlinear Cointegration (James Duffy, Oxford University)
Seasonal and Trend Modelling for Forecasting (Harry Haupt, University of Passau)

Course leader

Martin Wagner (University of Klagenfurt)
Dietmar Bauer (Bielefeld University)

Target group

The summer school’s primary target audience are PhD students. However, subject to capacity the school also welcomes post-doctoral researchers (from universities, research institutes or (central) banks) and professionals with a strong mathematical and statistical background seeking to extend their knowledge base in time series analysis. A good preliminary knowledge of the main work-horses in time series analysis, e.g., estimation and specification of vector autoregressive models, is expected.

Course aim

Each half-day will be dedicated to one topic in two 90-minute lectures. Presentations will roughly follow a common layout introducing the main theory of the area, potentially providing examples and discussing software available and finally indicating relevant questions at the research frontier.

Credits info

2 EC
The number of credits awarded depends on the students' home universities.

Fee info

EUR 550: PhD students
EUR 700: Others