Dr Gelly Mitrodima

This course is available on the MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Financial Mathematics, MSc in Financial Statistics, MSc in Financial Statistics (Research), MSc in Health Data Science, MSc in Mathematics and Computation, MSc in Operations Research & Analytics, MSc in Quantitative Methods for Risk Management, MSc in Statistics 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. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

How to apply

Priority is given to students from the Department of Statistics and those with the course listed in their programme regulations.

Students should check that they meet the prerequisites before applying, but do not need to provide a written statement. Statements will not strengthen an application and are not read.

Deadline for application: Interested students should apply as soon as possible after course selection opens on Thursday 24 September and no later than 5.00pm on Friday 9 October.

Course lecturers will aim to make initial offers to students on LSE For You by Friday 13 October.

For queries contact: Stats-Msc@lse.ac.uk

A good undergraduate-level knowledge of statistics and probability is required. Prior programming experience is not necessary; however, students with no previous experience in R are expected to complete the online pre-sessional R course offered by the Digital Skills Lab before the start of term (https://moodle.lse.ac.uk/course/view.php?id=8714).

The course begins with an introduction to fundamental time series models (AR, MA, ARMA; ARCH and GARCH models for financial time series), covering trend removal and seasonal adjustment, model selection and estimation, and forecasting. The second half focuses on multivariate and high-dimensional time series, including VARMA and its seasonal variant, and factor modelling for vector and matrix-valued time series. R examples are integrated throughout the lecture notes, and R applications are explored in the exercises.

9 hours of computer workshop and 20 hours of lectures in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Students are expected to complete weekly problem sets throughout the Winter Term.

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

Exam (100%), duration: 120 Minutes in the Spring exam period.

The course is assessed by a written examination (100%).