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, 15 March 2016
- Responsible department
- Department of Statistics
Entry requirements
A Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university. Also required is 90 credits in statistics and the course Econometric Theory and Methodology, 15 credits.
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.
Other directives
The course is included in the Master's programme in statistics.