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STAT30010

Academic Year 2024/2025

Time Series (STAT30010)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Wagner Barreto-Souza
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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 time-series 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:

- Introduction and basic concepts
- Autoregressive Moving Average (ARMA) models: definition and estimation of parameters
- Forecasting
- Autoregressive Integrated Moving Average (ARIMA) models: Identification and diagnostic checking
- Multivariate time series
- Cointegration

If time permits, we will also cover

- Generalised AutoRegressive Conditional Heteroskedasticity (GARCH) models
- Count time series modelling

About this Module

Learning Outcomes:

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 Box-Jenkins 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...

Student Effort Hours:
Student Effort Type Hours
Lectures

18
Tutorial

12
Computer Aided Lab

4
Specified Learning Activities

10
Autonomous Student Learning

75
Total

119

Approaches to Teaching and Learning:
Lectures, tutorials and computer labs.

Requirements, Exclusions and Recommendations
Learning Requirements:

Familiarity with basic probability concepts such as Probability distribution, Expectation, Variance, Covariance and Correlation. Knowledge of the main probability distributions (normal distribution, chi-square, ...). Basic linear algebra (vectors, matrices).

Learning Recommendations:

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). Knowledge of linear models and least square estimation which would be achieved upon completion of "Data Modelling for Science" STAT20070 or "Predictive Analytics I" STAT30240 would be beneficial.


Module Requisites and Incompatibles
Incompatibles:
ECON30540 - Advd Econometrics: Time Series, STAT40700 - Time Series Analysis - Act App, STAT40860 - Time Series (online)

Additional Information:
Students must have confidence and experience in the theory and use of expected values and covariances of random variables. Lack of proficiency in this area is the primary cause of difficulty in this module.


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Assignment 1: theoretical and computational questions. Week 5, Week 6 Alternative linear conversion grade scale 40% No
15
 No
Assignment(Including Essay): Assignment 2: theoretical and computational questions. Week 11, Week 12 Alternative linear conversion grade scale 40% No
15
 No
Exam (In-person): Final Exam End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
70
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

R.H. Shumway & D.S. Stoffer, Time Series Analysis and Its Applications: With R Examples, Springer, 2006 (2nd edition).
J.D. Cryer & K-S. Chan, Time Series Analysis: With Applications in R, Springer, 2008 (2nd edition).
P.J. Brockwell & R.A. Davis, Time Series: Theory and Methods, Springer, 2006 (2nd edition).
B. Pfaff, Analysis of Integrated and Cointegrated Time Series with R, 2008 (2nd edition).

Name Role
Moisés Chavira Flores Tutor
Pedro Menezes De Araújo Tutor
Atefeh Moradi Tutor
Ms Charlotte Stammers Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12 Mon 11:00 - 11:50
Autumn Lecture Offering 1 Week(s) - 5 Mon 11:00 - 11:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 12:00 - 12:50
Autumn Tutorial Offering 1 Week(s) - Autumn: Even Weeks Tues 17:00 - 17:50
Autumn Tutorial Offering 2 Week(s) - Autumn: Even Weeks Thurs 17:00 - 17:50
Autumn Laboratory Offering 1 Week(s) - 5, 7, 9, 11 Tues 17:00 - 17:50
Autumn Laboratory Offering 2 Week(s) - 5, 7, 9, 11 Wed 17:00 - 17:50
Autumn Laboratory Offering 3 Week(s) - 5, 7, 9, 11 Wed 15:00 - 15:50
Autumn Laboratory Offering 4 Week(s) - 5, 7, 9, 11 Tues 17:00 - 17:50
Autumn Laboratory Offering 5 Week(s) - 5, 7, 9, 11 Wed 17:00 - 17:50
Autumn Laboratory Offering 6 Week(s) - 5, 7, 9, 11 Wed 15:00 - 15:50
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