BAN430 Forecasting

Autumn 2018

Spring 2019
  • 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 as a good way as possible. For example, theoretically, the value of a stock depends on future dividends and if you can make better forecasts of dividends then can price stocks better. Forecasting macroeconomic variables are important as they influence tax revenue which is important for the spending opportunities for the government. Bad forecasts may have substantial negative effects on the real economy through bad decisions. Retail businesses may want to forecast sale volumes which 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
    • 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


  • Required prerequisites

    Basic skills in statistical inference.

  • Requirements for course approval

    The participants shall work with compulsory assignments during the course. These include theory as well as data analysis. Course approval is given on the basis of these assignments.

  • Assessment

    The final grade will be based on two major projects that accounts for 50 % each. The students can work in groups (1-2 students in each group).

  • Grading Scale


  • Computer tools

    The course contains use of the statistical packages in R.

  • Literature

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


ECTS Credits
Teaching language

Spring. Offered spring 2019.

Course responsible

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