Autumn 2018Spring 2019
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.
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
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.
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).
The course contains use of the statistical packages in R.
Forecasting: Principles and Practice, Rob J. Hyndman and George Athanasopoulos, 2nd edition (available at https://otexts.org/fpp2/).
- ECTS Credits
- Teaching language
Spring. Offered spring 2019.
Professor II Johan Lyhagen, Department of Business and Management Science.