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: Statistics G1F
  • 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
  • Entry requirements:

    30 credits in statistics

  • Responsible department: Department of Statistics

Learning outcomes

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.

Reading list

Reading list

Applies from: Spring 2011

Some titles may be available electronically through the University library.

  • Cryer, Jonathan D.; Chan, Kung-sik Time series analysis : with applications in R

    2. ed.: New York: Springer, cop. 2008

    Find in the library


Last modified: 2022-04-26