NMAK14013U Modeling dependence in discrete time (AAM)
MSc Programme in Mathematics
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
In this course we study some basic topics from classical time series analysis. We show show how second order dependence in a stationary process manifests in the time and in the frequency domains, i.e. in the autocorrelation function and in the spectral density of the data. We discuss the use of ARMA and GARCH models and related statistical problems, including the estimation of the autocorrelation function, the properties of the periodogram and parameter estimation for ARMA and GARCH processes. We discuss different forms of prediction in a time series. We also consider the extremogram and the extremal index as measures of extremal dependence in a time series. These quantities are useful for describing clusters of extremes.
Knowledge:To understand relevant time series models (FARIMA,
GARCH, etc.) and their applications, in particular to financial
data.
To understand the relation between the autocovariance function and
the spectral distribution.
To know basic estimation procedures and their properties.
To know extremal dependence measures in a time series.
Skills:
At the end of the course the student shall be able to
analyse stationary time-discrete processes in the time domain
(autocovariance and autocorrelation functions) and their spectal
distribution.
He/she will also be able to use software packages for time series
analysis
such as SAS and R.
Competence:
The student will be able to read monographs and articles on time
series analysis and he/she will be able to conduct independent
research on real-life time series data.
Lecture notes
- Category
- Hours
- Exam
- 50
- Lectures
- 45
- Preparation
- 111
- Total
- 206
As
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Continuing Education - click here!
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentTwo projects (theoretical problems and simulations). Both count for 50%.
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
One internal examiner
- Re-exam
- Resubmission of the two projects from the continuous assessment. The projects must be resubmitted at the end of the reexamination week.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK14013U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 3
- Schedule
- B (Mon 8-12 + Tues 13-17 + Fri 8-12)
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Thomas Valentin Mikosch (7-767274787c6c7149766a7d7137747e376d74)
Office: 04.3.10