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. The software package Stata, with its programming language Mata, will be used for this purpose. Stata and Mata will not be taught, but students can easily acquire the necessary skills by following one of the numerous tutorials available online.

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:

1. Home work problems count 20% of final mark.
2. The midsession exam is 2 hours, closed book, and counts 30% of final mark.
3. The final exam is closed book and counts 50% of final mark.

8 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