|Module Title||MULTIPLE TIME SERIES|
|Co-ordinator||Dr J A Lane|
|Course delivery||Lecture||19 x 1hour lectures|
|Seminars / Tutorials||3 x 1hour example classes|
|Assessment||Exam||2 Hours 2 hour written examination||75%|
|Project report||About 2,000 words plus supporting graphs, tables etc||25%|
|Resit assessment||2 Hours (written examination) Project passed: assessment as above, project mark carried forward Project failed: 2 hour written examination - 100%||100%|
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.
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.
** 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