Credits 
5 
Subject 
Statistics & Actuarial Science 
Level 
3 
School 
Mathematics and Statistics 
Semester 
Semester One 
Module Coordinator 
Dr Michael SalterTownshend 
This course provides an opportunity for students to learn some basic techniques in Time Series Analysis. A Time Series is a set of measurements taken at regular intervals over a period of time. Time series pervade the worlds of economics and finance e.g. equity prices, (CPI) inflation, GNP, GDP, derivative prices, oil prices, etc. Although the techniques of Time Series Analysis are used by many people, from meteorologists to astronomers, this course will mainly focus on examples in the world of economics and finance. This course will show how to model such data and how using those models forecasts and predictions can be made.Time series analysis is not a new subject, however there have been many advances in recent years and the 2003 Nobel prize in Economics was awarded to Engle and Granger for their work in timeseries econometrics. This course will cover both traditional methods and more modern approaches to Time Series Analysis. Upon completion of this course students should have mastered techniques that are extremely valuable for careers in the analysis of economic and financial data. Topics covered include, among others: Stationarity, ARIMA models, Parameter Estimation, Forecasting and Cointegration. Additional topics may be included which may vary from year to year.
Upon completion of this module students will be able to:
1. Identify the stationarity properties of a time series,
2. Model the time series using BoxJenkins ARIMA techniques,
3. Estimate parameters for ARIMA models using a variety of procedures,
4. Produce forecasts for a given time series,
5. Be familiar with additional topics such as cointegration, vector auto regressive models...
 Hrs/Semester 
Lectures  24 
Tutorial  12 
Computer Aided Lab  4 
Specified Learning Activities  10 
Autonomous Student Learning  75 
Total Workload  125 
 % of Final Grade  Timing 
Continuous Assessment: Assignments  15  Throughout the Semester 
Continuous Assessment: Computer Lab Homeworks  10  Throughout the Semester 
Examination: Final 2 hour examination  75  2 hour End of Semester Exam 
Compensation 
This module is not passable by compensation 
Resit Opportunities 
End of Semester Exam 
Remediation 
If you fail this module you may repeat, resit or substitute where permissible 
Module Requisites and Incompatibles 
PreRequisite : 
Required : 
CoRequisite : 
Incompatibles : 
Additional Information : 
Equivalent Modules 

Prior Learning 
Requirements: 
Familiarity with basic probability concepts such as Probability distribution, Expectation, Variance, Covariance and Correlation. Knowledge of the main probability distributions (normal distribution, chisquare, ...). Basic linear algebra (vectors, matrice). 
Excluded: 

Recommended: 
Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of "Inferential Statistics" STAT20100.
Basic knowledge in linear algebra (vectors, matrices). A knowledge of linear models and least square estimation (STAT30240/STAT30250) can be a plus but is not compulsory. 
Timetabling information is displayed only for guidance purposes, relates only to 2017/2018, and is subject to change.
For an explanation of the week ranges referred to below, please click here:
Computer Aided Lab  Semester 1 Offering 1  Tue: 17:0017:50 (7, 8, 9, 10) 
Computer Aided Lab  Semester 1 Offering 2  Wed: 17:0017:50 (7, 8, 9, 10) 
Lectures  Semester 1 Offering 1  Mon: 11:0011:50 (Sem1: All Weeks) 
Lectures  Semester 1 Offering 1  Wed: 12:0012:50 (Sem1: All Weeks) 
Lectures  Semester 1 Offering 1  Thu: 17:0017:50 (Sem1: All Weeks) 