Understand the importance of knowing the mechanisms that generate univariate time-series data.
Learn the basic tools for making forecasts including software (E-Views).
Learn and understand methods to investigate dynamic characteristics of time-series data.
Gain practical experience in analysing, modelling and forecasting a time series and reporting the results in course paper.

PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. THE INFORMATION PROVIDED HERE IS BASED ON THE COURSE SETUP PRIOR TO THE CORONAVIRUS CRISIS. AS A CONSEQUENCE OF THE CRISIS, COURSE COORDINATORS MAY BE FORCED TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS. This course will cover static and dynamic 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 time-series and with the empirical skills to estimate, test, and forecast the most appropriate dynamic models.

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

This course can be chosen as an elective by students in the Bachelor program of IBE and IES. Students must have a good and solid foundation in empirical econometrics and applied statistics, in particular statistics including knowledge about the multiple linear regression model.

James H. Stock and Mark W. Watson : Introduction to Econometrics provides a good level indication of the prerequisites knowledge students must have to be able to follow this course successfully.

An advanced level of English.