Postgraduate Course: Time Series Analysis (MATH11062)
Course Outline
| School |
School of Mathematics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 11 (Postgraduate) |
Credits |
7.5 |
| Home subject area |
Mathematics |
Other subject area |
Financial Mathematics |
| Course website |
None |
|
|
| Course description |
This half-course aims to provide student with an introduction to time series analysis, including models with applications in finance. Presenting the material in the form of a specific half-module allows for greater flexibility and makes it available to postgraduate students on other programmes who would benefit. |
Entry Requirements
| Pre-requisites |
|
Co-requisites |
|
| Prohibited Combinations |
|
Other requirements |
None
|
| Additional Costs |
None |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 2, Not available to visiting students (SS1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
Summary of Intended Learning Outcomes
On completion of this course the student should be able to:
- demonstrate knowledge of, and a critical understanding of, the main concepts of time series analysis
- demonstrate knowledge of, and a critical understanding of, the main properties of MA, AR, ARMA, ARIMA, and RW models
- use least squares, maximum likelihood and other methods to fit time series models to the data
- select proper model(s) using e.g. AIC or BIC
- fit trend and seasonal trend to the data, and fit time series models to the residuals
- understand methods used to produce forecasts
- understand ARCH, GARCH and other nonlinear time series models and their applications for modelling of financial data
- understand time series data well, and perform basic calculations and summaries of time series data
- understand and critically assess time series models fitted by computer packages
- use a range of time series models to produce forecasts
- communicate meaningfully and productively with others (including practitioners and professionals in the financial services industry) on time series analysis issues
- Demonstrate the ability to earn independently
- Manage time, work to deadlines and prioritise workloads. |
Assessment Information
| Examination 100% |
| Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
| Not entered |
Contacts
| Course organiser |
Dr Sotirios Sabanis
Tel: (0131 6)50 5084
Email: S.Sabanis@ed.ac.uk |
Course secretary |
Mrs Katherine Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk |
|
copyright 2010 The University of Edinburgh -
1 September 2010 6:19 am
|
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