Student Pages
Postgraduate course

Time Series

  • ECTS credits10
  • Teaching semesterSpring
  • Course codeSTAT211
  • Number of semesters1
  • Language


  • Resources

Main content

Teaching semester

Every second spring - odd-numbered years.

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.

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.

Exam information