The module provides an analytical introduction to time-series econometrics and the challenges that present themselves with the analysis of time-series economic data. Traditional econometric techniques such as Ordinary Least Squares (OLS) are poorly suited to the estimation of economic models or data which exhibit non-stationary processes. This module provides an introduction to econometric methods that are suitable for stationary and non-stationary time-series analyses.
The module is both analytical and practitioner based providing students with the knowledge, understanding, application and interpretation of time-series techniques using specialist econometric software. The module equips students with the analytical tools to carry out advanced time-series econometrics work at a later stage of their degree programme.
The topics considered in the module include:
• Stationary and non-stationary data; trend- and difference-stationary processes, stationary autoregressive models, multivariate stationary models, spurious regression, cointegration, ADF tests, forecasting.
Total contact hours: 30 hours
Private study hours: 120
Total study hours: 150
This module is compulsory for all Single Honours degree programmes in Economics.
This module is optional for all Joint Honours degree programmes in Economics.
This module is not available to students across other degree programmes in the University.
Method of assessment
In Course Test 1 (45 minutes) (15%)
In Course Test 2 (45 minutes) (15%)
Examination, 2 hours (70%)
Reassessment Instrument: 100% exam
The main text for the module is:
J Wooldridge (2016), Introductory Econometrics: A Modern Approach, 6th ed, Cengage
Other examples are:
C Dougherty (2011), Introduction to Econometrics, 4th ed, Oxford University Press
D Gujarati and D Porter (2010), Essentials of Econometrics, 4th ed, McGraw-Hill
G Maddala and K Lahiri (2009), Introduction to Econometrics, 4th ed, Wiley
M Verbeek (2012), A Guide to Modern Econometrics, 4nd ed, Wiley
See the library reading list for this module (Canterbury)
By the end of the module, you will be able to:
* identify and abstract the properties of time-series data and relevant data sources.
* demonstrate knowledge and understanding of statistical, graphical and numerical data analyses.
* apply time-series econometrics to economic data using specialist econometric software.
* interpret and analyse empirical results obtained from the application of time-series econometric to economic data.
* perform data transformations and diagnostic tests relevant to the analysis of time-series data.
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Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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