ECON3208: Applied Econometric Models

2x15% Assignments. We were given a dataset and a stata file, and a sheet of questions to answer. These questions were then tested in a multiple choice moodle quiz. A bit strange, but nothing difficult.

25% Group Project. This is an 8 page empirical research paper where we were given a dataset used in a paper, and then asked to answer the same question as the paper using the techniques described in lectures. They randomly assigned the groups within tutorials, or you could choose to do the project by yourself. 5% is from a team assessment, so if you've got a bad group you can flame them there.

45% Final Exam. 50 multiple choice questions in 2.5 hours.

ECON2206 (or be enrolled in a Data Science degree and take MATH2831).

A lot of statistics courses get (deservedly in my opinion) a bad rap for being deliberately obfuscatory and hard to follow. I can't really say the same about this course. Sure, it still has difficult content that takes time to wrap your head around, but it never felt like there was a need for the big conceptual leaps and blind acceptances of theorems that I felt were present in ECON2206. This course doesn't hold your hand, but it takes time exploring the big ideas before launching into a more in-depth exploration of the topics.
This course is essentially an extension to ECON2206, where you spend most of your time patching up the holes left behind by that course. Most of the lectures start with the premise of "here's something wrong with a particular regression, how can we fix it?", and then take a very logical path in ruling out what can and can't be done to fix that issue. This resulted in a course that felt a bit disjointed and lacking an identity of its own - you're constantly going back and forward between issue and solution, and not really considering if that solution would bring any issues as well.

2 x 1.5 hour lecture per week. 1 x 1.5 hour tutorial per week.

4/5.

Full lecture and tutorial recordings available.

Mike Keane, 3/5. Mike taught the start and end of the course. His slides were a bit dense, but alright overall.

Fanghua Li, 4/5. Fanghua was pretty good for this course, can't really have asked much from her.

Full slides provided.

3/5.

2021/T3

71 CR