The overall aim of this course is to provide students with an advanced understanding of the principles underlying estimation methods and hypothesis tests in econometrics. The course first covers basic probability theory, then introduces asymptotic approximation theory, and finally moves to statistics and econometrics. To focus on general ideas, the course abstracts from real-world issues and turns to the laboratory/sandbox called mathematics.

In addition to mathematical derivations, the properties of certain techniques will be investigated through computer simulation.

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

The assessment consists of three parts: assignments, a mid session exam, and a final exam. See course outline on RSE web site for weighting.

10 hours per week including class time

An honours degree in Economics with H2A or higher, or completion of a Graduate Diploma in Economics with an exit grade of Merit or better

For further information please refer to course website