Syllabus for Time Series Econometrics

Tidsserieekonometri

Syllabus

  • 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: 2022-10-14
  • Revised by: The Department Board
  • Applies from: Autumn 2023
  • Entry requirements:

    120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.

  • 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

Reading list

Applies from: Autumn 2023

Some titles may be available electronically through the University library.

  • Hamilton, James D. Time series analysis

    Princeton, N.J.: Princeton Univ. Press, cop. 1994

    Find in the library