Entry requirements

120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.

Learning outcomes

The course is an introduction to time series econometrics for second-cycle studies and treats basic themes in modern time series analysis. A student who has taken the course should:

* have a solid knowledge about basic themes in modern time series analysis

* know and be able to use concepts and notation that is frequently used in time series analysis

* know and be able to use different probabilistic results for serially dependent observations

* be familiar with different methods to estimate time series models

* be able to choose on appropriate model and estimation method for a given time series

* be able to interpret the results of an fitted model

* be aware of limitations and possible sources of errors in the analysis

Content

Difference equations. White noise, stationarity and ergodicity. Stationary ARMA processes: The Box-Jenkins approach. Prediction: Wold's theorem, test for predictive accuracy. Vector autoregressive models. Maximum likelihood estimation. Asymptotic theory for serially dependent vector autoregressive processes. Bayesian analysis. State-space model and the Kalman filter. Models of nonstationary time series: unit root and deterministic time trends. Cointegraton.

Instruction

Teaching is given in the form of lectures and tutorial classes.

Assessment

The examination takes place through a written examination at the end of the course and compulsory written assignments. The grading scales are: failed, passed and passed with distinction.

Other directives

The course is included in the Master's programme in statistics.