Time series analysis and prediction

MET520 Time series analysis and prediction

  • Topics



    • Overview of time series methods in finance, business administration and economics 
    • Linear models (ARIMA)
    • Forecasting
    • Volatility models (GARCH etc.)
    • Other time series models and methods (cointegration, etc)
    • Other topics on times series can be treated if time allows and according to the interests and needs of the participants. Possible topics are e.g. multivariate ARIMA models, non-linear models, non-parametric models, state-space models and Bayes methodology in time series analysis.

  • Learning outcome

    Learning outcome

    Economic decisions are often based on the analysis of time series data. The purpose could be to investigate historical development, to forecast future development, to test an economic theory, to price a financial asset, to allocate a portfolio, to explain effects of an intervention of or changes in a policy variable or to control a dynamic system. This course gives an introduction in the statistical modelling and prediction of economic and financial time series.


    After completing the course the students will be able to

    • understand the central ideas of time series analysis and forecasting
    • follow academic literature in applied economics using time series analysis
    • implement the most common techniques to real-world forecasting problems
    • use time series analysis to test economic theory empirically
    • model financial volatility
    • model non-stationary time series variables that evolves simultaously over time

  • Teaching


    Lectures and computer lab.

  • Required prerequisites

    Required prerequisites

    Basic skills in statistical inference (recommended)

  • Requirements for course approval

    Requirements for course approval

    The participants shall work with compulsory assignments during the course. These include theory as well as data analysis and is given the grade pass/not pass. Course approval (the right to attend the written final exam) is given on the basis of these assignments.

  • Assessment


    An term paper on a topic decided by the instructor and PhD student.

  • Grading Scale

    Grading Scale

    Grading: Pass / fail

  • Computer tools

    Computer tools

    The course contains use of the statistical packages R and/or Gretl.

  • Semester



  • Literature


    Enders: Applied econometric time series 3ed. Wiley 2010.


    Cowpertwait and Metcalfe: Introductory time series with R. Springer 2009.


    Journal articles will be used for the final evaluation paper. These will be determined by the topic chosen for the term paper and will be papers where time series methods are applied to different topics in business, economics and finance.


    Recommended readings:

    Mills: The econometric modelling of financial time series, 2 ed. Cambridge 1999.

    Tsay: Analysis of financial time series. Wiley 2002.


ECTS Credits
Teaching language
Spring, Autumn

Course responsible

Jonas Andersson, Department of Finance and Management Science