Module Identifier MA37610  
Academic Year 2001/2002  
Co-ordinator Dr J Lane  
Semester Intended for use in future years  
Next year offered N/A  
Next semester offered N/A  
Pre-Requisite MA46610  
Course delivery Lecture   19 x 1 hour lectures  
  Seminars / Tutorials   3 x 1 hour example classes  
Assessment Project report   About 2,000 words plus supporting graphs, tables etc   25%  
  Exam   2 Hours 2 hour written examination   75%  
  Resit assessment   2 Hours (written examination) Project passed: assessment as above, project mark carried forward Project failed: 2 hour written examination - 100%   100%  

General description

Many of the most important uses of time series analysis concern the relationships between two or more series. The ARIMA models introduced in the module on Time Series and Forecasting will be extended to cater for interventions and transfer functions and to multiple ARIMA models. The module includes a short project.


To study models for relating two or more time series and to gain practical experience of their analysis by means of a project.

Learning outcomes

On completion of this module, a student should be able to:


1. TRANSFER FUNCTIONS AND INTERVENTION ANALYSIS: Regression-autogression models; ordinary least squares estimation and the Mann-Wald theorem. Transfer functions: interventions; impulse response function; stability and gain; crosscorrelation function, prewhitening, identification of transfer functions; estimation and diagnostic checking. Forecasting.
2. MULTIPLE TIME SERIES: The multivariate ARMA model; stationarity and invertibility, marginal models, equivalent models. Cross-covariance matrices. Co-integration.

Reading Lists

** Supplementary Text
G E P Box, G M Jenkins & G C Reinsel. Time Series Analysis, Forecasting and Control. 3rd. Prentice-Hall
J D Hamilton. Time Series Analysis. Princeton University Press
M G Kendall & J K Ord. Time Series. Edward Arnold
C W J Granger & P Newbold. Forecasting Economic Time Series. 2nd. Academic Press