Course content

Autoregressive and moving average based models for stationary and non-stationary time series. Model identification, parameter estimation and forecasting. ARCH and GARCH models for volatility. Cointegration. State space models, linear dynamic models and the Kalman filter.

Learning outcome

The course gives basic knowledge of models for series of stochastically dependent observations in time, with applications in engineering and finance. The student will through the exercises be able to apply the theory to analyse time series data.

Learning methods and activities

Lectures and exercises on a computer. The lectures may be given in English. Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination 80% and selected parts of the exercises 20%. The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination.

Compulsory assignments

• Exercises

Recommended previous knowledge

The course is based on TMA4240/4245 Statistics or equivalent. The course demands some degree of maturity in Statistics and we recommend to have also TMA4265 Stochastic Processes and TMA4267 Linear Statistical Models in advance.

Course materials

Will be announced at the start of the course.

Credit reductions