This module is not currently running in 2024 to 2025.
The module aims to give students a solid understanding of the basic econometric tools that are often used in the empirical finance literature. The module also develops the IT skills of the students so that students are able to implement sophisticated statistical techniques to model, analyse and forecast financial data by means of Eviews (econometric software). Students will also improve their ability to critically evaluate the use of econometrics in the academic finance literature.
Topics covered include:
• Dummy variables in linear regression models
• Time series models (ARIMA models)
• Forecasting
• Unit root tests
• Simulation analysis
The module will be taught by lectures, computer workshops and private study.
Total Contact Hours: 32
Private Study Hours: 118
Main assessment methods
Eviews VLE Test : 20%
Examination – 2 hours: 80%
Reassessment methods
100% examination.
Essential reading:
Brooks, C. (2014) Introductory Econometrics for Finance (3rd Ed.), Cambridge: Cambridge University Press.
Other reading:
Patterson, K. (2000). An Introduction to Applied Econometrics: A Time Series Approach. Andover: Palgrave.
Wooldridge, J. M. (2016) Introductory Econometrics: A Modern Approach (6th Ed.), Boston, MA: Cengage Learning.
See the library reading list for this module (Medway)
8. The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 Demonstrate a comprehensive knowledge and understanding of the fundamentals of the statistical theory underlying basic econometric models and techniques.
8.2. Formulate and validate econometric models to test financial theories and hypotheses.
8.3 Critically apply relevant knowledge and IT skills to analyse financial data and draw conclusions regarding the behaviour of financial markets.
8.4 Comprehend and critically evaluate the use of econometrics in the published academic finance literature.
9. The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 Select and critically apply a variety of econometric techniques, both autonomously and collaboratively.
9.2 Undertake modelling of data using statistical software.
9.3 Demonstrate numeracy and problem solving skills for the interpretation and manipulation of quantitative data
9.4 Effectively communicate information, arguments and analysis to both specialist and non-specialist audiences.
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