Dr Yining Chen COL 7.06

This course is available on the BSc in Actuarial Science, BSc in Data Science, BSc in Mathematics with Data Science, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course is not capped, any student that requests a place and meet the criteria will be given one.

2nd year statistics and probability

Students who have no previous experience in R are required to complete an online pre-sessional R course from the Digital Skills Lab before the start of the course.

The course introduces the student to the statistical analysis of time series data and simple time series models, and showcase what time series analysis can be useful for. Topics include: autocorrelation; stationarity, trend removal and seasonal adjustment; AR, MA, ARMA, ARIMA; estimation; forecasting; model diagnostics; unit root test; introduction to financial time series and the ARCH/GARCH models; and if time permits, basic spectral analysis. The use of R for time series analysis will also be covered.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Winter Term. This course includes a reading week in Week 6 of Winter Term.

Written answers to set problems will be expected on a weekly basis.

Peter J. Brockwell and Richard A. Davis, Introduction to Time Series and Forecasting

Robert H. Shumway and David S. Stoffer, Time Series Analysis and Its Applications: With R Examples

Christopher Chatfield, The Analysis of Time Series

Ruey S. Tsay, An Introduction to Analysis of Financial Data with R

Peter J. Brockwell and Richard A. Davis, Time Series: Theory and Methods

Christian Francq and Jean-Michel Zakoïan, GARCH Models: Structure, Statistical Inference and Financial Applications

Exam (90%, duration: 2 hours) in the spring exam period.
Coursework (10%).