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.

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.