ST418 Half Unit
Advanced Time Series Analysis
This information is for the 2025/26 session.
Course Convenor
Prof Clifford Lam
Availability
This course is available on the MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Financial Mathematics, MSc in Health Data Science, MSc in Marketing, MSc in Mathematics and Computation, 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 uses controlled access as part of the course selection process.
How to apply: Priority is given to students from the Departments of Statistics and those with the course listed in their programme regulations.
Students should check that they meet the pre-requisites in the course guide before applying, but do not need to provide a written statement. Providing a statement will not aid a student's chances of being accepted onto a course and statements are not read.
Deadline for application: Due to the nature of the method of application, interested students should apply as soon as possible after the opening selection and no later than 10.00am on Friday 26 September.
Course lecturers will aim to make initial offers to students on LSE For You by Friday 26 September.
For queries contact: Stats-Msc@lse.ac.uk
Requisites
Additional 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=8714).
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: VARMA and its seasonal version; Factor modelling for vector and matrix-valued time series. R examples will be given in lecture notes, and R applications will be investigated in exercises.
Teaching
10 hours of computer workshops and 20 hours of lectures in the Winter Term.
This course has a reading week in Week 6 of Winter Term.
Formative assessment
Students will be expected to produce solutions to 9 problem sets in the WT.
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: 120 Minutes in the Spring exam period
The course will be assessed by an examination (100%).
Key facts
Department: Statistics
Course Study Period: Winter Term
Unit value: Half unit
FHEQ Level: Level 7
CEFR Level: Null
Total students 2024/25: 30
Average class size 2024/25: 15
Controlled access 2024/25: NoCourse 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
- Self-management
- Problem solving
- Communication
- Application of numeracy skills
- Specialist skills