Time Series Econometrics

7.5 credits

Syllabus, Master's level, 2ST111

Education cycle
Second cycle
Main field(s) of study and in-depth level
Statistics A1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Department Board, 14 October 2022
Responsible department
Department of Statistics

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


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.


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


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.