Clifford Lam COL.6.09

This course is available on the MSc in Applicable Mathematics, 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.

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).

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.

20 hours of lectures and 10 hours of computer workshops in the WT.

Week 6 will be used as a reading week.

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: 2 hours) in the spring exam period.