BAN430 Forecasting

• Topics

  BAN430 will provide students with an 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 main time series topics that will be covered:

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

  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 more correctly. Forecasting macroeconomic variables is essential, as they influence tax revenues, 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:

  Knowledge

  • Explain the central ideas of time series analysis and forecasting.

  Skills

  • 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.

• Teaching

  Teaching consists of interactive sessions and lectures given at campus. Most of the curriculum will be supported by online based modules containing short videos, exercises and notes. The students will work with data labs containing exercises and cases. Students will have to hand in an assignment to document competence in the use of statistical software and reporting of results.

• Recommended prerequisites

  It is recommended that students have taken a course in R (BAN420 or equivalent).

• Required prerequisites

  Basic skills in statistical inference.

• Credit reduction due to overlap

  None.

• Compulsory Activity

  Approved hand-in assignment.

• Assessment

  The final grade is based on an individual 8 hour take home exam.

  The assessment has been changed, and course approval from Spring 2023 or later is required to retake the exam.

• Grading Scale

  A - F

• Computer tools

  R.

• Literature

  Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition (available at https://otexts.org/fpp3/https://otexts.org/fpp3/).

  Lecture notes.

  Scientific papers.