Syllabus for Time Series Analysis

Tidsserieanalys

Syllabus

  • 7.5 credits
  • Course code: 2ST093
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Statistics G1F

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2007-05-31
  • Established by:
  • Revised: 2021-09-09
  • Revised by: The Department Board
  • Applies from: Spring 2022
  • Entry requirements:

    At least 15 credits from Statistics A, 30 credits

  • 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

Content

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.

Instruction

Lectures

Assessment

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."

Syllabus Revisions

Reading list

Reading list

Applies from: Spring 2022

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

    Mandatory

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