This is a course in advanced time series econometrics with a focus on the models used in macroeconomics and finance. It will cover four main classes of models: state space models, nonlinear time series models, factor models, and models for use with mixed frequency data. The focus of the course will be on using these models in practice. To this end, the lectures will discuss the models and their properties and show how to estimate and forecast with them using R. In addition to 14 hours of lectures, the course will include four hours of computer labs where students will gain experience in working with these models. The course will be assessed through a final exam.
We are very thankful to Gary Koop, who previously taught this course and has developed the core teaching materials for this class!
Lecturer & Course Organiser: Niko Hauzenberger (niko.hauzenberger@strath.ac.uk; nhauzenb.github.io)
Lecturer: Ping Wu (ping.wu@strath.ac.uk; pingwu.org)
Tutor: TBC
Office Hours: Immediately after the lecture or online via Zoom (in this case email in advance)
This course takes place in block 4 (semester 2) over six weeks and involves lectures and computer sessions. The lectures are given by Niko Hauzenberger and Ping Wu, while the computer sessions are given by […]. You can find out more about the teaching team from their websites.
The course will cover four main sets of models:
Assessment will be through a final 2-hour exam in the April/May Diet worth 100% of the grade.
The primary reading for the course is: Ghysels, E. and Marcellino, M. (2018) Applied Economic Forecasting Using Time Series Methods.
Another good textbook which covers much of the course material is: Tsay, R. (2010) Analysis of Financial Time Series (third edition).
Make sure you download R and learn how to use it BEFORE for the first computer session. Instructions provided in Lab 0.
Additional lab material will be added soon.