Syllabus for Time Series Econometrics

Tidsserieekonometri

  • 7.5 credits
  • Course code: 2ST111
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1N

    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: 2010-06-03
  • Established by: The Department Board
  • Revised: 2021-10-15
  • Revised by: The Department Board
  • Applies from: week 35, 2022
  • Entry requirements: 120 credits including 90 credits in statistics.
  • Responsible department: Department of Statistics

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. Vector autoregressive models. Maximum likelihood estimation. Asymptotic theory for serially dependent vector autoregressive processes. Bayesian analysis. State-space model and the Kalman filter. Models of nonstationary time series: unit root and deterministic time trends. Cointegraton.

Instruction

Teaching is given in the form of lectures and tutorial classes.

Assessment

The examination takes place through a written examination at the end of the course and compulsory written assignments. The grading scales are: failed, passed and passed with distinction.

Other directives

The course is included in the Master's programme in statistics.

Reading list

The reading list is missing. For further information, please contact the responsible department.