Autumn 2023Spring 2024
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
- 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.
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
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.
It is recommended that students have taken a course in R (BAN420 or equivalent).
Basic skills in statistical inference.
Credit reduction due to overlap
Approved hand-in assignment.
The final grade is based on an individual 8 hour take home exam.
The asseessment has been changed, and course approval from spring 2023 is required to retake the exam.
A - F
Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition (available at
). https://otexts.org/fpp3/ https://otexts.org/fpp3/
- ECTS Credits
- Teaching language
Spring. Offered Spring 2023.
Assistant Professor Sondre Hølleland, Department of Business and Management Science.