Time Series Analysis

BEA526 Time Series Analysis

Spring 2024

  • Topics

    The course will give the students in-depth knowledge in time series models and skills on how to analyse and implement them. Properties of the models are investigated. Methods for statistical inference and forecasting based on the models are treated. The course will cover

    • Difference equations
    • Basic properties of time series processes
    • ARMA processes
    • Forecasting theory
    • Estimation: Maximum likelihood and GMM
    • Modeling and testing for non-stationarity
    • Multivariate time series models
    • Cointegration
    • State space models and the Kalman filter
    • Conditional heteroskedasticity

  • Learning outcome

    Knowledge: After completing the course, the student

    • knows the fundamental concepts of time series modelling
    • knows how to make statistical inference from time series models
    • knows how to use time series models to forecast a time series

    Skills: After completing the course, the student

    • can analyse the properties of a time series model
    • can formulate an appropriate time series model for a given problem and data set
    • can use a time series model on data to make statistical inference and forecast future values of a variable

    Competence: After completing the course, the student

    • will be able to understand and utilise articles in the academic literature where time series methods are used
    • will be able to co-work with researchers using time series methods

  • Teaching

    The course is a reading course and is not given as a regular course

  • Restricted access

    Admission is restricted to one student since the course is presently given on a trial basis.

  • Recommended prerequisites

    • Knowledge in probability and statistical inference corresponding to the book "Probability and Statistical Inference" by Hogg, Tanis and Zimmerman.
    • Be able to use matrix algebra

  • Assessment

    A term paper on a topic agreed on with the course responsible.

  • Grading Scale


  • Computer tools


  • Literature

    • Hamilton, J, (1994) Time Series Analysis, Princeton University Press
    • Selected articles


ECTS Credits
Teaching language

Course responsible

Jonas Andersson