BAN430 Forecasting

Autumn 2021

  • Topics

    Overview of time series methods used in business administration and economics for the purpose of forecasting and forecasting evaluation with emphasis on applied forecasting:

    • The decomposition of Time Series (season, cycle, trend)
    • Linear models (ARIMA) including exponential smoothing
    • Dynamic regression models
    • Forecasting, including Judgmental forecasts

    Many decisions, especially in economics and business, depend on future values of variables of interest. Hence, there is a need to be able to forecast these variables in the best possible way. For example, theoretically, the value of a stock depends on future dividends, and if you can make better forecasts of dividends, then you can price stocks better. Forecasting macroeconomic variables is essential, as they have influence on tax revenue, which is important for a government's spending opportunities. Bad forecasts may have substantial negative effects on the real economy, through bad decisions. Retail businesses may want to forecast sales volumes, which do not only depend on season, but on advertising as well, for the purpose of holding an optimal level of the product. The purpose of this course is to give the students tools to make forecast and an understanding of the modelling and forecasting of economic variables.

  • Learning outcome

    After completing the course the students are able to:


    • understand the central ideas of time series analysis and forecasting


    • decompose a time series into its components
    • graphically present time series
    • model a real-world time series using an appropriate time series model and use it for forecasting
    • evaluate forecast performance and to identify the components of forecast errors

    General Competence:

    • use R and appropriate packages
    • read scientific papers in forecasting
    • develop a research question for a master thesis
    • know the structure and requirements for a master thesis

  • Teaching


    It will be possible to follow the course digitally.

  • Required prerequisites

    Basic skills in statistical inference.

  • Credit reduction due to overlap


  • Requirements for course approval


  • Assessment

    The final grade will be based on a major project. The students can work in groups (1-2 students in each group).

    Students will be working on the project in week 12-14.

  • Grading Scale


  • Computer tools

    We will make use of the statistical packages in R.

  • Literature

    Forecasting: Principles and Practice, Rob J. Hyndman and George Athanasopoulos, 2nd edition (available at

    Lecture notes.

    Scientific papers.


ECTS Credits
Teaching language

Spring. Offered Spring 2021.

Course responsible

Adjunct Professor Johan Lyhagen, Department of Business and Management Science.