Understand the importance of knowing the mechanism that generated the data regarding the phenomenon about which a forecast is required;
Learn the basic tools for making forecasts including software (E-Views)
Learn and understand methods to investigate dynamic relationships between a series and influential exogenous variables;
Gain practical experience in analysing, modelling and forecasting a time series and reporting the results in course paper.

This course will cover static forecasting models, models with trends and seasonals, and cover the Box-Jenkins methodology for modeling cycles in stationary data. The course will provide students with a thorough understanding of dynamic time-series and with the empirical skills to estimate, test, and forecast the most appropriate dynamic model.

The book by Diebold (2014), 'Forecasting' (available online).

This course can be chosen as an elective by students in the Bachelor program of IB, INF, IBE and IES. Students are expected to have a good foundation in quantitative methods, in particular statistics including knowledge about the multiple regression model.

Bowerman and O’Connell (2007), Business Statistics in Practice, 4th ed., McGraw-Hill, Chapters 1 to 12 give a good indication of the topics that students should have covered in earlier courses.
An advanced level of English.