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

Postgraduate Course: Foundations of Econometrics (CMSE11388)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryFoundations of Econometrics aims to provide a thorough training in basic econometric methods, to enable students to critically assess applied work and conduct their own quantitative research using appropriate econometric techniques. Together with Applications of Econometrics, this course provides the foundations required to conduct quantitative research at the PhD level.
Course description This course exposes students to a thorough and rigorous treatment of basic econometric methods. Students learn the assumptions and properties of basic estimators through mathematical proofs. Most of the material will focus on linear models; however, some nonlinear cases, such as limited dependent variable models and generalized methods of moments, are also discussed. Examples in Finance and Economics will be used to provide context. This course serves as a foundation for the students' understanding of more advanced estimation techniques that will be delivered in Applications of Econometrics.

- An introduction to linear regression
- Interpreting and comparing regression models
- Heteroskedasticity and autocorrelation
- Endogenous regressor, instrumental variables and GMM
- Maximum likelihood estimation and specification tests
- Models with limited dependent variables
- Models based on panel data

Formal teaching occurs in lectures and tutorials. However, much of the learning will be the result of students' own reading and reflection, and preparation for weekly assignments, weekly class tests, and the final examination. Students will also be exposed to a commonly used statistical software (Stata) through independent learning.

A high level of student participation is expected, through discussion in class and among peers outside of class. Cooperation amongst students when completing exercises and during exam preparation is strongly encouraged.

Students are assumed to have good background knowledge of matrix algebra as well as statistical and distribution theory.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesPermission from the Course Organiser to attend
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 156 )
Assessment (Further Info) Written Exam 50 %, Coursework 40 %, Practical Exam 10 %
Additional Information (Assessment) This course has 3 methods of assessment:
1. Continuous written assessment worth 40% of total weight
2. A Practical Written Assignment worth 10% of the total weight
3. A written examination worth 50% of the total weight

Coursework assessments (40%) includes weekly group homework assignments and individual class tests. For group homework assignments, students are required to complete tutorial questions and submit their answers before each tutorial class. Individual class tests (20%) will take place at the beginning of tutorial classes, and will take up to 20 minutes.

The practical exam component (10%) is a 1,000-word empirical discussion of an empirical paper.

The final written examination (50%) will comprise three sections. The first two sections will focus on proofs, concepts, and theories. The third section will focus on interpretation and critical evaluation of empirical results.
Feedback Feedback will be provided in the form of solutions to tutorial questions, homework questions, and quizzes. Summative feedback will include specific feedback for individual groups on their coursework submissions and generic examination feedback.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Foundations of Econometrics2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a critical understanding of basic econometric methods, their assumptions, and the consequences of the violations of these assumptions on empirical results
  2. Apply knowledge of basic econometrics to contemporary issues in Finance empirical research
  3. Critically evaluate econometric approaches currently used in Finance empirical research
Reading List
Verbeek, M. 2012. A guide to modern econometrics. 4th ed. West Sussex: John Wiley & Sons.

Supplementary textbooks
Greene, W. H., 2012. Econometrics Analysis, 7th ed., NJ: Prentice Hall/Pearson.
Hayashi, F. 2000. Econometrics. NJ: Princeton University Press.
Stock, J. H., and Watson, M. M., 2012. Introduction to Econometrics. 3rd ed. London: Pearson Education.

Note: The structure of the course and the notation used will follow Verbeek (2012), which is the core textbook. Some proofs and presentations in lectures will be drawn from Hayashi (2000), particularly when the links between GMM and other estimators are presented. Note that basic knowledge of matrix algebra is required to understand these textbooks. Students can refresh their knowledge of matrix algebra as well as foundation statistics through reading Appendices A and B of Verbeek (2012).

Additional readings will be suggested in the course of the lectures. They will mostly be examples of how the methods presented in class are applied to empirical research in Finance.
Additional Information
Graduate Attributes and Skills Research and enquiry
Personal and intellectual autonomy
Personal effectiveness
KeywordsEconometrics,linear regression,linear models,finance
Course organiserMr Ben Sila
Course secretaryMr Ciaran Masson
Tel: (0131 6)50 9945
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