Time Series
Course offered :
- Current semester
- Next semester
Current programmes of study
Course offered by
| Number of credits | 10 |
| Course offered (semester) | Every second spring - odd-numbered years. |
| Schedule | Schedule |
| Reading list | Reading list |
Aim 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.
Course offered (semester)
Every second spring - odd-numbered years.
Language of Instruction
English
Recommended previous knowledge
MAT121 Linear Algebra, STAT210 Theory of Statistical Inference, and STAT111 Statistical Methods or STAT200 Applied Statistics
Assessment methods
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.