Postgraduate Course: Time Series (MATH11131)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course offers an introduction to the theory of time series analysis and forecasting. The aim is to learn the basics of the mathematical theory and to understand the wide applicability of the subject matter through some real-world applications, primarily in economics and finance. |
| Course description |
- Revision of basic definitions in Statistics including expectation, variance, autocovariance and autocorrelation.
- Properties of moving average and autoregressive models.
- Estimation of parameters of moving average and autoregressive models.
- Introduction to ARCH, GARCH and other nonlinear time series models
and their applications for modelling of financial data.
- Estimation of parameters of moving average, autoregressive and nonlinear models.
- Forecasting using Kalman filters.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For admission to this course, a good understanding of probability at undergraduate level is required. If in doubt, please consult with the Course Organiser. |
Course Delivery Information
| Not being delivered |
Learning Outcomes
- demonstrate knowledge of, and a critical understanding of, the main concepts of time series theory;
- demonstrate knowledge of, and a critical understanding of, the main properties of moving average and autoregressive models;
- use least squares, maximum likelihood and other methods to fit time series models to the data;
- understand ARCH, GARCH and other nonlinear time series models and their applications for modelling of financial data;
- demonstrate an understanding of, and critical assessment of, time series models fitted by computer packages;
- demonstrate an understanding of, and critical assessment of, methods used to produce forecasts;
- use a range of time series models to produce forecasts.
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Reading List
Brockwell-Davis: Introduction to Time Series and Forecasting, 2nd Edition, Springer, 2002
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | TS |
Contacts
| Course organiser | Dr Sotirios Sabanis
Tel: (0131 6)50 5084
Email: S.Sabanis@ed.ac.uk |
Course secretary | Mrs Julie Hands
Tel: (0131 6)50 4885
Email: Julie.Hands@ed.ac.uk |
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