Postgraduate Course: Econometrics for Risk Analytics (CMSE11647)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course covers econometrics techniques used in causal and predictive analyses. Its main objective is to equip students with quantitative skills commonly needed at financial organisations and in empirical analyses for MSc dissertations. The course also lays a sound foundation for future research at the PhD level. The methods studied are illustrated with examples of their applications in banking and risk management. |
| Course description |
This course provides knowledge required to give students a broad understanding of methods that can be used in a variety of empirical analyses in banking and risk management. The course also equips students with practical skills to undertake dissertations, company sponsored projects, quantitative assignments, and tasks involving econometrics in financial institutions and other organisations.
In general, four types of models are considered: basic linear model, linear models accounting for endogeneity, panel data, and models with limited dependent variables.
Outline content
- OLS: Review and Limitations
- Instrumental Variables
- Panel Data (Fixed and Random Effects)
- Difference-in-Differences
- Generalised Methods of Moments
- Binary Response Models
- Multinomial Unordered Models
- Multinomial Ordered Models
Student Learning Experience
The approaches studied will be illustrated by means of practical examples in lectures, seminars and tutorials. The limitations of the methods taught and potential ways to overcome them will be discussed with students, who will be challenged to come up with their own ideas to solve the problems analysed.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | Students MUST also take:
Statistics for Analytics (CMSE11624)
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| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, 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:
200
(
Lecture Hours 16,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
170 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
70% Written Exam (Individual) - Assesses all course Learning Outcomes
30% Project report (Individual) - Assesses course Learning Outcomes 2,3,4 |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on assessments within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand the objectives and the main characteristics of each regression model studied on the course.
- Understand and critically assess the results of econometric models.
- Understand and critically discuss the implications of the results of econometric models.
- Understand and critically evaluate the limitations of the models studied.
- Select the most suitable regression model vis-à-vis the characteristics of the data and the problem analysed.
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Reading List
Core text(s)
Wooldridge, Jeffrey (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press, 2nd ed.
Verbeek, Marno (2012). A Guide to Modern Econometrics. John Willey and Sons, 4th ed.
Cunningham, Scott (2021). Causal Inference: The Mixtape. Yale University Press.
Hill, Campbell (2012). Using SAS for Econometrics. John Wiley and Sons.
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Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact. |
| Keywords | Econometrics,Banking,Causal Inference,Categorical Dependent Variables |
Contacts
| Course organiser | Dr Fernando Moreira
Tel: (0131 6)51 5312
Email: Fernando.Moreira@ed.ac.uk |
Course secretary | |
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