Postgraduate Course: Research Methods in Carbon Finance (CMSE11205)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | This course aims to turn out students who are able to approach dissertations with all the necessary research methods training to address most carbon finance issues as well as enter any organisation and have the skills and knowledge on the key areas to research and evaluate carbon finance. |
| Course description |
This course has been created specifically for the MSc in Carbon Finance, reflecting the cross-disciplinary nature of the programme, as well as the niche aspects of Carbon Finance as an emerging discipline. Carbon Finance is a rapidly growing niche field which requires an understanding of statistics/econometrics as well as an understanding of the unique nature of carbon trading and investments. In addition to special focus on carbon financial analysis, this course will provide you with applications and motivations for statistical/econometrics model building in modern finance as a whole. The course content in this regard is similar to an undergraduate statistics course and will also be backed up by practical demonstration on an econometrics software package; hence the material should be accessible for both students with strong quantitative backgrounds and those who do not have this background but are willing to put an appreciable level of effort into learning the class material. The skills developed on this course are transferable and may also be very useful for your dissertation.
Syllabus
Qualitative Research Methods
Basic Data Handling
Working with data
Introduction to Probability
Sampling and sampling distributions
Statistical Inference: Hypothesis Testing for Single Populations
Statistical Inference: Hypothesis testing about two populations
Correlation
Classical linear regression model
Dummy variables
CLRM Assumptions and Diagnostics
Modelling long run relationships in finance
Student Learning Experience
Statistics like other branches of applied mathematics is best learnt through individual application. Students should endeavour to study recommended materials in advance of a lecture as well as after the lecture if still unclear. Attendance of lectures does not guarantee the understanding of concepts. For Weeks 4,5,10 and 11, lectures will take place in the computer lab, thus providing the opportunity to instantly apply concepts to empirical problems. Students are advised to be engaged with this process. Reading materials should also be studied in advance of the lab sessions.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2015/16, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 20,
Summative Assessment Hours 2,
Other Study Hours 125,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
0 )
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| Additional Information (Learning and Teaching) |
Directed Learning and Independent Learning Hours 125
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Individual Assessment worth 40%
Final exam worth 60% |
| Feedback |
Summative marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.
Feedback will comprise students interpreting concepts during lab sessions, first and second assessment Feedback and generic examination Feedback. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | | | Main Exam Diet S1 (December)Main Exam Diet S2 (April/May)Resit Exam Diet (August)Outwith Standard Exam Diets JanuaryOutwith Standard Exam Diets FebruaryOutwith Standard Exam Diets MarchOutwith Standard Exam Diets AprilOutwith Standard Exam Diets MayOutwith Standard Exam Diets JuneOutwith Standard Exam Diets JulyOutwith Standard Exam Diets AugustOutwith Standard Exam Diets SeptemberOutwith Standard Exam Diets OctoberOutwith Standard Exam Diets NovemberOutwith Standard Exam Diets DecemberResit Exam Diet (April/May Sem 1 resits only) | Exam | : | |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify, critically evaluate, select, justify and apply appropriate research methods to relevant research questions, in order to ensure that the evidence generated, its analysis and the conclusions drawn from it are valid and reliable.
- Present the findings of research in an academic manner.
- Define, critically evaluate and apply the major tools used by financial economists (correlation, regression and time series analysis) =.
- Link statistical and econometrics theory with empirical applications.
- Conduct empirical analysis with econometrics software packages such as EViews 8.
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Reading List
Koop, G. (2006) Analysis of Financial Data, John Wiley & Sons Ltd: Chichester
Brooks, C. (2008) Introductory Econometrics for Finance, Cambridge University Press: Cambridge
Cortinhas, C. and Black, K. (2012) Statistics for Business and Economics, John Wiley & Sons Ltd: Chichester
Booth, W. C., Colomb G. G. and Williams, J. M. (2008) The Craft of Research, 3rd Edition, University of Chicago Press
Business Research Methods, Alan Bryman and Emma Bell, 2nd Edition. This is a text book which covers many research methods used in business and is used in many business schools as the standard research method text.
Research Methods for Business Students, Mark Saunders, Philip Lewis and Adrian Thornhill, 4th Edition. Another research methods text used in business schools.
Understanding Social Statistics, Jane Fielding and Nigel Gilbert, 2nd Edition. An accessible guide to statistics commonly used in business research. Valuable for those looking to undertake statistical analysis. |
Additional Information
| Graduate Attributes and Skills |
Cognitive Skills:
After completing this course, students should be able to:
- Develop analytical, numerical and problem solving skills
- Critically assess existing understanding in a defined area of knowledge;
- Recognize qualitative and quantitative techniques appropriate to the analysis of particular circumstances;
- Apply a range of relevant qualitative and quantitative research methods;
- Use relevant literature and data reference materials.
Subject Specific Skills:
After completing this course, students should be able to:
- Understand and use statistics notations and theory to solve a wide range of problems in Finance and particularly Carbon Finance
- Understand various research approaches which they can apply to their dissertation projects
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Gbenga Ibikunle
Tel: (0131 6)51 5186
Email: Gbenga.Ibikunle@ed.ac.uk |
Course secretary | Miss Malgorzata Litwinska
Tel: (0131 6)51 6363
Email: Maggie.Litwinska@ed.ac.uk |
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