MA37610 - MULTIPLE TIME SERIES

Module Identifier	MA37610
Module Title	MULTIPLE TIME SERIES
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.

Aims

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:

describe and analyse transfer function noise models;
use a computer package to identify, estimate and check models relating two time series;
construct forecasts using transfer function/noise models;
explain the need for pre-whitening in the identification of transfer functions;
analyse low order multivariate ARMA models;
describe cointegration and explain its implications.

Syllabus

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

Books
** 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