STAT40860 Time Series (online)

Academic Year 2021/2022

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: Stationarity, ARIMA models, Parameter Estimation, Forecasting and Cointegration. Additional topics may be included which may vary from year to year.

Show/hide contentOpenClose All

Curricular information is subject to change

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
Specified Learning Activities

24

Autonomous Student Learning

75

Online Learning

24

Total

123

Approaches to Teaching and Learning:
Video lectures posted each week that walk though module content, blending theory with example exercises.
Practice problem sheets posted each week to enable self-assessment of learning outcomes. Sample solutions for these will be posted approximately one week after each problem set. Coding based problem sets posted with solutions again following.
All content delivered using the VLE which includes a monitored discussion forum with topics created for each weeks lecture material and each problem set. 
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:
STAT30010 - Time Series Analysis, STAT40700 - Time Series Analysis - Act App

Additional Information:
Students must have confidence and experience in the theory and use of expected values and covariances of random variables.


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. Throughout the Trimester n/a Graded No

40

Examination: 2 hour end of semester examination 2 hour End of Trimester Exam No Graded No

60


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.

J.D. Cryer & K-S. Chan, Time Series Analysis: With Applications in R, Springer, 2008 (2nd edition).
Name Role
Mr John O'Sullivan Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

There are no rows to display