Postgraduate Course: Time Series (MATH11131)
|School||School of Mathematics
||College||College of Science and Engineering
||Availability||Not available to visiting students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Mathematics
||Other subject area||None
||Taught in Gaelic?||No
|Course description||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.
Entry Requirements (not applicable to Visiting Students)
||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.
|Additional Costs|| None
Course Delivery Information
|Not being delivered|
Summary of Intended 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.
||- 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.
||Brockwell-Davis: Introduction to Time Series and Forecasting, 2nd Edition, Springer, 2002
|Course organiser||Dr Sotirios Sabanis
Tel: (0131 6)50 5084
|Course secretary||Mrs Julie Hands
Tel: (0131 6)50 4885