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 Module Code: MAT3012  Module Title: STATISTICAL METHODS FOR BUSINESS AND FINANCE
Module Provider: Mathematics Short Name: MS333 Previous Short Name: MS333
Level: HE3 Module Co-ordinator: GODOLPHIN JD Dr (Maths)
Number of credits: 15 Number of ECTS credits: 7.5
 
Module Availability

Spring semester

Assessment Pattern

Unit(s) of Assessment
Weighting Towards Module Mark( %)
2 hour unseen examination
75%
1 piece of coursework
12.5%
1 class test or coursework
12.5%
Qualifying Condition(s) 
A weighted aggregate mark of 40% is required to pass the module.

Module Overview
This module focuses on several areas of statistics that are relevant to business and management. Fundamental concepts in time series and forecasting are covered.
The module also includes one of the following:
(i)      An introduction to Bayesian decision theory with applications to business problems;
(ii)    A selection of more advanced topics in time series including strategy for selecting ARIMA models.

Practical work will be based on R.
Prerequisites/Co-requisites

MAT2002 General Linear Models.
Module Aims
The aim is to teach basic techniques in univariate time series. The course provides students with the tools to enable them to analyse and interpret analyses of data arising from such time series.
Learning Outcomes

The aim is to teach basic techniques in univariate time series along with more advanced topics in time series or decision theory. The course provides students with the tools to enable them to analyse and subsequently interpret data arising from time series and to analyse decision problems.
Module Content
  1. Time series and forecasting: Linear filters, differencing and exponential smoothing. The Box-Jenkins approach, introduction to autoregressive and moving average models. Applications and business finance.
 
One or other of the following topics will be covered in the remainder of the course.
 
  1. Bayesian decision theory. Decision trees for simple and business problems. Subjective probability and utility. Utility of money. Consistency and coherence. Scoring rules. Utility functions. Risk aversion. Multi-attribute utility. Group decision making. Sensitivity analysis.
 

Building strategy for ARIMA models. Autocorrelation and partial autocorrelation functions. Trend function, steady and linear growth models. Seasonality. Business and finance case studies including: stock price deviations, UK monthly temperatures, kilometres flown by UK airlines.

Methods of Teaching/Learning

3 contact hours per week for 10 weeks. Mainly lectures but including some supervised computer lab sessions.
Selected Texts/Journals
C. Chatfield, The Analysis of Time Series: An introduction. (6th ed.), Chapman & Hall, (2004).
 
G.E.P. Box, G.M. Jenkins, and G.C. Reinsel, Time series analysis: Forecasting and Control. (3rd ed.), Holden Day, (1994).
 
M.J. Crawley, Statistics: An Introduction using R. Wiley, (2005).
 
W.N. Venables and B. D. Ripley, Modern Applied Statistics with S-PLUS, Springer, (1999).
 
J.D. Cryer, Time Series Analysis. Duxbury Press, (1986).
 
P. Brockwell and R. Davis, An introduction to Time Series and Forecasting. (2nd ed.) (2002).
 
W.N. Venables and B. D. Ripley, Modern Applied Statistics with S-PLUS, Springer, (1999).
 
Lindley, D.V. Making Decisions.
 
Smith, J.Q. Decision Analysis: A Bayesian approach.
 
French, S. Readings in Decision Analysis
Last Updated

04.11.08

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