# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014 -- ARCHIVE as at 1 September 2013 for reference onlyTHIS PAGE IS OUT OF DATE

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# Postgraduate Course: Time Series (MATH11131)

 School School of Mathematics College College of Science and Engineering Course type Standard Availability Not available to visiting students Credit level (Normal year taken) SCQF Level 11 (Postgraduate) Credits 10 Home subject area Mathematics Other subject area None Course website 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.
 Pre-requisites 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. Additional Costs None
 Not being delivered
 - 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.
 Coursework: 30% Examination: 70%
 None
 Academic description Not entered Syllabus - 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. Transferable skills Not entered Reading list Brockwell-Davis: Introduction to Time Series and Forecasting, 2nd Edition, Springer, 2002 Study Abroad Not entered Study Pattern Not entered Keywords TS
 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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