Examination arrangement

Course content

Economic forecasting is a key ingredient of decision making both in the public and in the private sector. This course provides an overview of both theory and applications. PhD students will learn basic and advanced forecasting techniques using state of the art method, software, and databases. The course course covers a broad overview of time series forecasting: descriptive statistics, graphical analysis, regression analysis, forecast evaluation, forecast combinations, ARIMA models, VAR models, VECM models, Bayesian VAR, TAR/STAR models Regime Switch models, State Space models, Models with mixed frequencies of data, Big data and Machine Learning Methods, Quantile Regression, GARCH models and Risk Forecasting. The course also covers forecast evaluation and forecast combination. The course will cover practical implementation of models in Eviews and R along with the FRED database on macroeconomics and finance. Students are free to chose other software (e.g. Phyton etc.) an databases (e.g. Eikon Datastream, Bloomberg) in their presentations.

Learning outcome

In this course, the candidate will acquire key knowledge in modern forecasting methods in economics and finance. The candidate will receive training to make a lecture of selected topics and present these. The candidate will also receive training in writing and presenting selected data, methods, and implementation of methods from the course. This will be an important part of the general PhD training for the candidate. The candidate will also get acquaintance to usage of databases and statistical software.

Learning methods and activities

The course will consist of a mixture of traditional lectures and practical exercises over intensive seminars in September, October, and November. Lectures will also be available digitally. Lectures will be given by faculty.

Compulsory assignments

• Active participation in lectures

Further on evaluation

In order to get the course approved, the students need to make lectures on a selected method from the course together with specific data and show how to implement the models and interpret the results. The candidates need to demonstrate knowledge in using specific data, methods, and software implementation. The quality of these presentations will be evaluated by an external examiner and the lecturer. The presentations will be recorded.

Recommended previous knowledge

Basic courses within economics and finance. General knowledge within mathematics, statistics and computer science.

Course materials

Will be given at course start