Time Series Analysis
Syllabus, Bachelor's level, 2ST093
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Statistics G1F
- Grading system
- Fail (U), Pass (G), Pass with distinction (VG)
- Finalised by
- The Department Board, 9 September 2021
- Responsible department
- Department of Statistics
At least 15 credits from Statistics A, 30 credits
A student that has completed the course should
? have deeper knowledge of statistical theory and methods particularly common problems in economical social sciences especially economics.
? be able to estimate models for time-series data.
? be able to interpret the results of an implemented statistical analysis
? be aware of limitations and possible sources of errors in the analysis
? have ability to present results in oral and written form
Overview of forecasting. Models for time series: Time-dependent seasonal components. Autoregressiva (AR), moving average (MA) and mixed ARMA-modeller. The Random Walk Model. Box-Jenkins methodology. Forecasts with ARIMA and VAR models.
Dynamic models with time-shifted explanatory variables. The Koyck transformation . ?Partial adjustment? and ?adaptive expectation? models. Granger's causality tests. Stationarity, unit roots and cointegration. Modelling of volatility: ARCH - and the GARCH-models.
The examination comprises a written test at the end of the course and compulsory assignments, (laboratory sessions). Three grades are awarded for the course: not passed, passed, and passed with distinction.
"If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator."