The course provides a (medium-level) introduction into major aspects of time series analysis, both theoretically and by means of complementing practical applications. Students are able to specify adequate models stationary and integrated time series, and use the estimated models for analysis, including forecasting, but also structural analysis. Students are also aware of potential pitfalls and problems. 

The course combines lectures and practice sessions, with the practice sessions consisting of discussions of both "pencil and paper" as well as computer exercises using both simulated and real-world data. Many software packages and/or programming languages/environments are being used in time series analysis. In the practice sessions, the focus lies on MATLAB and R. 

IntroductionDescriptive Time Series AnalysisNaive Forecasting MethodsHilbert SpacesStationary ProcessesSpectral AnalysisParameter EstimationIntegrated ProcessesRegression with Integrated ProcessesVAR Cointegration AnalysisStructural VAR ModelsThe item list on the previous slide is encompassing (potential) – and as such too much for our allocated time.We will have to make a “sensible” selection, conditional upon:Prior knowledgeSpecific interests (compatible with prior knowledge) The mathematics/statistics of TSE tends to become relatively involved relatively quickly: Dependent data (maybe even heterogeneous) and potentially nonlinear estimation problems...

Understanding of basic mathematical statistics and some linear algebra is most useful for successful participation in the course. Some familiarity with real analysis and stochastic processes would facilitate the understanding of technical details but is not required. To some extent and within bounds the course can be adapted to the prior knowledge of the participants.

Teaching MaterialsDuring the semester slides, background material – and of course the exercise sheets and data that we provide for the practice sessions – will be uploaded to the Moodle course.The slides do not contain proofs of mathematical results – these will be developed in the classroom on the blackboard.The course does not follow a specific textbook closely.There is a large number of good – partly specific, partly general – time series books, some are listed in the Moodle course.SoftwareThere is no unique market leader when it comes to software.There are programming languages or environments like:MATLAB, GAUSSPythonRThere are also (more or less) user-friendly and powerful (“clickable”) software environments like:EViewsStatagretlJMulti: closed-shop

Continuous assessment of the practice sessions:Tick the exercises you have solved and upload the solutions until Tuesday 5pm prior to the practice session in which the corresponding exercise sheet is discussed.During the exercise sessions you will be randomly selected to present your (uploaded) solutions (to ticked exercises).In order to be eligible for the two-part final exam you have to tick at least 50% of the exercises during the semester.Two-part final exam:Preparation of a small report (of a few pages only).Oral exam: Covering all topics discussed during the semester in the lectures and practice sessions as well as the small report. Individual dates to be agreed upon.

All topics covered in the lectures and practice sessions.