Time Series Econometrics
Syllabus, Master's level, 2ST111
- Code
- 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, 3 June 2010
- Responsible department
- Department of Statistics
Entry requirements
Bachelor's degree of 180 credits with 90 credits in statistics
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. Maximum Estimation. Asymptotic theory for serial dependent Vector autoregressive (BE) processes. Kalman filter: State.cpace representation Generalised Method of Moments. Models of non--stationary time series: unit root theory. Cointegraton. Time series models of heteroskedasticity ARCH, GARCH. Models of long memory time series: ARFIMA.
Instruction
Teaching is given in the form of lectures.
Assessment
The examination takes place through a written examination at the end of the course and compulsory written assignments. The grades that can be obtained in the course are: failed, passed and passed with credit, respectively.
Other directives
The course is included in the Master's programme in statistics.