Course content

Brief repetition of basic principles for planning and follow-up of financial activities, cost distribution, forecasting in various production contexts and based on activity analysis and main principles for keeping financial accounts with profit and loss and balance sheet. A key part of the course focuses on how analyzes of various forms of market organization (perfect competition, monopoly, oligopoly, etc.) as well as forecasts of market development trends and of other key macroeconomic framework conditions affect the company's decisions.

The course then addresses why and how companies should perform prediction and what forecasting tools are available. Analysis of trends, seasonality and cycles in data. Regression models. Autoregressive and moving average models (ARMA), ARIMA-models, VAR-models and GARCH-models. Model evaluation. Advanced models for prediction. Prediction models based on qualitative information. Practical implementation of prediction models in companies and organizations. Use of databases and statistical software in forecasting.

Learning outcome

After completing the course, the student should be able to analyze industrial markets and forecast key features in industrial markets. The student should be able to define a sample and describe data, understand and discuss empirical results, be able to make methodologically sound projections and be able to communicate conclusions and implications based on the analysis. The student should be able to write and present a term-paper based on the material learned in the course. The following minimum requirements for passing the course in terms of knowledge, skills and general competence are:

Knowledge: The student will have an overview of key features in industrial markets and about variation in market models within a context of the individual company's needs and strategies. Have good and up-to-date knowledge of quantitative models for market forecasts and projections.

Skill: The student should be able to analyze specific markets based on case studies and be able to make forecasts for trends / development of key market parameters such as. price, production volume, etc. The student should be able to use data, computer programs and analytical tools that are suitable for forecasting work. The student must be able to convey in a pedagogical way results from projection analyzes.

General competence: Deeper understanding of how industrial markets work, why companies need market forecasts, when these forecasts are needed and what kind of forecasts are needed in different decision-making situations and in different market constellations.

Learning methods and activities

Lectures and problem solving in class and individually. 4 lecture hours and 2 exericise hours per week. The course is given in the form of intensive lectures several hours per day, several days a week in a few weeks. The course can alternatively be taken as an adapted self-study. Significant use of practice time on case studies from reality must be expected.

Specific conditions

Required previous knowledge

Mathematics and statistics for economists / engineers at bachelor level.

Course materials

Several articles and one handbook on Econometric Forecasting