BAN430 Forecasting (E)

• 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. Therefore, it is necessary to forecast these variables as accurately as possible. 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 because they influence tax revenues, which are important for a government's spending. Bad forecasts may have substantial negative effects on the real economy through bad decisions. Retail businesses may want to forecast sales volumes, which depend not only on the season but also on advertising, to maintain an optimal inventory level. The purpose of this course is to give students tools for making forecasts and an understanding of the modelling and forecasting of economic variables. In workshops and lectures, examples are included to demonstrate how forecasting can inform decisions with implications for efficiency and sustainability.

• 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 model, then use it to forecast.
  • Evaluate forecast performance and identify the components of forecast errors.

  General Competence

  • Use R and appropriate packages.
  • Use forecasting to inform decisions with efficiency and sustainability implications.
  • Read scientific papers in forecasting.
  • Develop a research question for a master's thesis.

• Teaching

  Teaching consists of interactive sessions and on-campus lectures. 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 must submit an assignment for course approval. 

• Recommended prerequisites

  None.

• Required prerequisites

  Approved compulsory activity from SKL402 is required for students to be eligible to sit the assessment in this course. Students may take SKL402 and BAN430 in the same semester.

• Credit reduction due to overlap

  None.

• Compulsory Activity

  Approved hand-in assignment.

  Compulsory activities from Spring 2023 and later semesters are still valid.

• Assessment

  5-hour individual digital school exam with R.

• 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.

  Course website:https://holleland.github.io/BAN430/ https://holleland.github.io/BAN430/ 

  Scientific papers.

• Permitted Support Material

  Calculator

  One bilingual dictionary (Category I)

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support materialhttps://www.nhh.no/en/for-students/regulations/https://www.nhh.no/en/for-students/regulations/and https://www.nhh.no/en/for-students/examinations/examination-support-materials/https://www.nhh.no/en/for-students/examinations/examination-support-materials/