Time Series

Postgraduate course

Course description

Objectives and Content

This course gives an introduction to linear time series models, such as autoregressive, moving average and ARMA models. Moreover, it is shown how the empirical autocorrelation and partial correlation can be used to identify the model. The Durbin- Levinson, the innovation algorithm and the theory for optimal forecasts are explained. The last part of the course gives an introduction to methods of estimation. Empirical modelling using the AIC and FPE criteria is mentioned as is ARCH and GARCH models.

Learning Outcomes

The purpose of the course is to give an introduction to the analysis and use of time series models.

Semester of Instruction

Recommended Previous Knowledge
MAT121 Linear Algebra, STAT210 Theory of Statistical Inference, and STAT111 Statistical Methods or STAT200 Applied Statistics
Forms of Assessment
Oral examination.
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.