Prof Wai-Fung Lam

This course is compulsory on the MSc in Statistics (Financial Statistics) and MSc in Statistics (Financial Statistics) (Research). 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 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). In previous years we have been able to provide places for all students that apply but that may not continue to be the case.

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).

A broad introduction to statistical time series analysis for postgraduates: what time series analysis can be useful for; autocorrelation; stationarity; causality;  basic time series models: AR, MA, ARMA; ARCH and GARCH models for financial time series; trend removal and seasonal adjustment; invertibility; spectral analysis; estimation; forecasting. We will also discuss nonstationarity and multivariate time series if time permits.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Autumn Term. This course includes a reading week in Week 6 of Autumn Term. Exercises will be given out to do at home during Week 6.

Weekly exercises will be given.

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.

Exam (80%, duration: 2 hours) in the spring exam period.
Coursework (20%) in the AT.

The course will be assessed by an examination (80%) and a coursework (20%) involving case studies which will be submitted in AT.