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Advanced Time Series
(State Space) Models

This course will show how time series can be modelled and analysed. The aim is to provide understanding and insight into the methods used, as well as explaining the technical details. Statistical time series modelling will be demonstrated using the STAMP computer package and participants will be given the opportunity to use STAMP in class.

 

The Time Series Lab (TSL) program enables the score-driven approach to nonlinear time series to be implemented. There will be a wide range of applications, ranging from assessing the impact of the UK seat belt law, modelling volatility in financial time series and predicting the spread of coronavirus.

This module can be taken alone or as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

State space models and the Kalman filter.

The modelling of univariate and multivariate time series. Practical time series modelling using the TSL package. 

Practical time series modelling using the STAMP package.

By the end of this course, participants should have knowledge and ability to: 

Desired Skills

Engage in abstract thinking by extracting the essential features of complex systems to facilitate problem solving and decision-making.

Understand the nature of time series models and the way they are applied in practice.

Present, interpret and analyse information and results from STAMP and TSL.

By the end of this course, students should be able to: 

Structure

 

Advanced Time Series (State Space) Models is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.

 

The total number of contact hours is 15 hours. This leaves 85 hours for private study.

Lectures

 

 

 

This module consists of 2- hour lectures per day, for 5 consecutive days, plus a 1 - hour tutorial per day.

 There will be optional clinics on the last day of the course.