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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 courses taken on the MSc in Banking and Risk, 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed:
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, 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 20, Seminar/Tutorial Hours 9, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 116 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 100% -
assesses Learning Outcomes 1, 2, 3, 4 and 5.

Feedback Feedback

Feedback on formative assessed work will be provided within 15 working days of submission, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be made clear to students at the start of the academic year.

Feedback will comprise individual feedback on the assignments in the form of a report.

Explanations and solutions will be provided during the tutorial sessions immediately after the deadline for submission.
No Exam Information
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--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.

Resource List:
Additional Information
Graduate Attributes and Skills Cognitive Skills

On completion of the course students will be able to:
- perform quantitative analyses in accordance with the type of the data used
- plan and execute projects involving empirical research
- analyse the association among variables in data sets
- assess the relevance of the results of quantitative analyses

Subject Specific Skills

After completing this course, students should be able to:
- run tests on the suitability of econometric models
- interpret the outputs of econometric models
- evaluate the performance of econometric models
- use the statistical package SAS to run several types of regressions
KeywordsNot entered
Course organiserDr Fernando Moreira
Tel: (0131 6)51 5312
Course secretaryMs Rachael Tring
Tel: (0131 6)51 5467
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