This is an advanced course on econometric theory. It will stress fundamental ideas and general concepts, so that students can draw connections between various methods presented in other courses, and learn to derive key theoretical results from first principles.  Underlying mathematical concepts such as linear algebra and probability will receive careful treatment.

The course will also introduce students to the statistical programming environment R.  R is open source software developed by the statistical community.  Students will learn to complete basic programming tasks and produce graphical output.  R is a more sophisticated statistics environment than Eviews or the like, with a steeper learning curve.  Familiarity with the language will take students a step closer to modern, computationally intensive statistical methods used in business and academia.

On successful completion of this course, students will be able to:

• demonstrate understanding of the foundational concepts of econometric theory
• exhibit familiarity with key tools from probability theory and mathematical statistics as they apply to econometrics
• explain the relationship between many of the core models used in econometrics, and their advantages and disadvantages
• determine appropriate modelling strategies in different applied settings
• write simple programs in the R programming language

Assessment will consist of two assignments, a mid-term exam and a final exam.

The weighting is:

Assignment 1:  15%
Assignment 2:  25%
Mid-term Exam:  25%
Final Exam:  35%

Two lectures and one tutorial per week.

EMET2008 Econometrics II: Econometric Modelling.

For further information please refer to course website