Syllabus for Time Series Analysis
A revised version of the syllabus is available.
- 7.5 credits
- Course code: 2ST093
- Education cycle: First cycle
Main field(s) of study and in-depth level:
- Grading system: Fail (U), Pass (G), Pass with distinction (VG)
- Established: 2007-05-31
- Established by: The Faculty Board of Social Sciences
- Revised: 2010-12-17
- Revised by: The Department Board
- Applies from: Spring 2011
30 credits in statistics
- Responsible department: Department of Statistics
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.
- Latest syllabus (applies from Spring 2022)
- Previous syllabus (applies from Autumn 2021)
- Previous syllabus (applies from Spring 2020)
- Previous syllabus (applies from Spring 2011)
- Previous syllabus (applies from Spring 2008)
Applies from: Spring 2011
Some titles may be available electronically through the University library.
Cryer, Jonathan D.;
Time series analysis : with applications in R
2. ed.: New York: Springer, cop. 2008