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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Econometrics Applications in Banking (CMSE11315)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryThis course covers cross section and panel data techniques. Its main objective is to equip students with quantitative skills commonly needed at financial institutions and in empirical analyses used in MSc dissertations. The methods studied are illustrated with examples of their applications in banking.
Course description This course provides foundation knowledge that is required to:
1. give students a broad understanding of a variety of research questions and methodology used in empirical analyses in banking;
2. provide complementary information that is needed for students to benefit the most from other courses taken on the MSc Banking Innovation and Risk Analytics, and
3. equip students with practical skills to undertake dissertations, company sponsored projects, quantitative assignments and tasks at financial institutions.

In general, four types of models are taught: basic linear model, linear models accounting for endogeneity, panel data and models with limited dependent variables.

Outline Content

- OLS Review and Limitations
- Multicollinearity, Heteroscedasticity and Autocorrelation
- Instrumental Variables
- Panel Data (Fixed and Random Effects)
- Difference-in-Differences
- Generalised Methods of Moments
- Binary Response Models
- Multinomial Unordered Models
- Multinomial Ordered Models
- Tobit Model

Student Learning Experience

The approaches studied will be illustrated by means of practical examples in classes. The limitations of the methods taught and potential ways to overcome them will be discussed in lectures and tutorials. Students will be challenged to come up with their own ideas to solve the problems discussed.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites Students MUST also take: Statistics for Analytics (CMSE11624)
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 16, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 121 )
Assessment (Further Info) Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment) 70% exam (individual) - assesses all course Learning Outcomes
30% coursework (individual) - assesses course Learning Outcomes 2, 3 and 4
Feedback Formative: Feedback will be provided throughout the course.

Summative: Feedback will be provided on the assessments within agreed deadlines.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Econometrics Applications in Banking (CMSE11315)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand the objectives and the main characteristics of each regression model studied on the course
  2. Understand and critically assess the results of econometric models
  3. Understand and critically discuss the implications of the results of econometric models
  4. Understand and critically evaluate the limitations of the models used
  5. Select the most suitable regression model vis-à-vis the characteristics of the data and the problem analysed
Reading List
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.

Hill, Campbell (2012). Using SAS for Econometrics. John Wiley and Sons.
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.
KeywordsNot entered
Course organiserDr Fernando Moreira
Tel: (0131 6)51 5312
Course secretaryMiss Aoife McDonald
Tel: (0131 6)50 8074
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