• Explain the concepts, models, methods and tools of econometrics as discussed during the course.
• Perform an academically founded econometric analysis on an identified problem in practice.
• Select, estimate and apply a suitable econometric model, including the assumptions, of the model toward solving a problem founded in practise.
• Discuss and solve any important problems encountered in relation to the analysis (e.g. heteroscedasticity for a cross-sectional data set)
• Perform hypotheses testing of both simple and more composite hypotheses
• Apply the model for the purpose it was constructed for (e.g. testing a theory or making forecasts).
• Report the results and conclusion of the analysis both if the reader of the report is an econometrician and if the reader is a non-econometrician (e.g. the CEO).
• Evaluate an empirical study conducted by another person/researcher and read and interpret output from the SAS computer programme.

Some of the main tools of econometrics that are standard when analyzing data from fields such as industrial organization, economics and finance at the graduate level are introduced. Emphasis will be on analysis of economic data by means of statistical models, i.e. regression models and time series models. Some relevant software will be used in handling data introduced for computer based exercises and illustrations. Integration with the other courses at the line will take place via the selection of material for illustrations and exercises.
Some of the topics covered are:
The simple and multiple regression models and their interpretation.
Basic time series models (ARIMA), identification, estimation, testing and forecasting
Time series econometrics. More advanced topics (e.g. ARCH) only at the introductory level.

Literature:
James H. Stock and Mark M. Watson (2012), Introduction to Econometrics (3rd international edition); Pearson Education Limited;  ISBN:13  978-1-4082-6433-1 / ISBN10: 1-4084-6433-1