ST418 Half Unit
Advanced Time Series Analysis
This information is for the 2024/25 session.
Teacher responsible
Clifford Lam COL.6.09
Availability
This course is available on the MSc in Applicable Mathematics, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Financial Mathematics, MSc in Marketing, MSc in Operations Research & Analytics, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.
This course has a limited number of places (it is controlled access). Priority is given to students from the Departments of Statistics and those with the course listed in their programme regulations.
Pre-requisites
Good undergraduate knowledge of statistics and probability. Previous programming experience is not required but students who have no previous experience in R must complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745).
Course content
We start with the introduction of basic time series models (AR, MA, ARMA; ARCH and GARCH models for financial time series), trend removal and seasonal adjustment; model selection and estimation; forecasting. The second half of the course focus on multivariate and high dimensional time series: Factor modelling for vector and matrix-valued time series; Multivariate GARCH and regularisation methods. Simple examples of nonlinear time series models including threshold models. R examples will be given in lecture notes, and R applications will be investigated in exercises.
Teaching
20 hours of lectures and 10 hours of computer workshops in the WT.
Week 6 will be used as a reading week.
Indicative reading
Brockwell & Davis, Time Series: Theory and Methods; Brockwell & Davis, Introduction to Time Series and Forecasting; Box & Jenkins, Time Series Analysis, Forecasting and Control; Shumway & Stoffer, Time Series Analysis and Its Applications; Ruey S. Tsay, Multivariate Time Series Analysis: With R and Financial Applications; William W.S. Wei, Multivariate Time Series Analysis and Applications.
Assessment
Exam (100%, duration: 2 hours) in the spring exam period.
Key facts
Department: Statistics
Total students 2023/24: Unavailable
Average class size 2023/24: Unavailable
Controlled access 2023/24: No
Value: Half Unit
Course selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Problem solving
- Communication
- Application of numeracy skills
- Specialist skills